Using a quantum computer alongside a supercomputer, scientists have developed a breakthrough pathway for modeling the physics inside a fusion reactor. The world-first experiment could help clear a path to developing clean, abundant nuclear power and solving the global energy crisis, the researchers said.

Using hybrid quantum computing and artificial intelligence (AI) methods, scientists with IBM and Oak Ridge National Laboratory (ORNL) have blueprinted how to make tritium, an extremely rare isotope of hydrogen that's critical to the fusion process.

Although their research — uploaded June 29 to the preprint server arXiv — has not been peer-reviewed, the researchers say it's the first time that different kinds of computing elements have come together to propose the most effective way to create this material.

Fusion reactors are experimental power sources that create energy by fusing atomic nuclei. The heat produced in the subsequent nuclear reaction is then harnessed as energy. This method produces no carbon byproducts or long-lived radioactive waste, making it one of the cleanest potential forms of mass energy production.

It's projected that, at scale, a single fusion reactor could produce about 4 million times as much energy as a coal-burning facility and around four times the amount of energy as a modern nuclear fission reactor.

Current attempts at building a viable fusion reactor have resulted in numerous laboratory experiments that prove the technology works, with magnetic confinement reactors, such as tokamaks, widely considered the front-runner. But many engineering challenges remain before the first commercial reactors could come online.

Turning seawater into fuel

The base fuel for nuclear fusion reactors is a hydrogen isotope called deuterium, which is commonly found in seawater. It's estimated that there are 33 grams of deuterium in every cubic meter of seawater.

But deuterium is only half of the equation. Nuclear fusion also requires tritium — a heavier hydrogen isotope — and the fusion released from just 1 gram (0.04 ounces) of deuterium-tritium fuel equals the energy from about 2,400 gallons (9,100 liters) of oil, according to the U.S. Department of Energy.

Unfortunately, tritium, a radioactive isotope, is extremely rare; only 44 pounds (20 kilograms) of it is produced on Earth each year, and its 12-year half-life makes it difficult to use in nuclear power plants.

Instead, scientists must painstakingly produce tritium in nuclear reactors by bombarding lithium atoms with neutrons. It's then superheated and bound with powerful magnets into a whirling ring of plasma within a tokamak, a special fusion chamber designed to shape and heat plasma using magnetic fields.

Scientists add more deuterium and then bash the tritium and deuterium together, causing them to fuse into helium. The force of this reaction creates heat that's converted into energy.

The current bottleneck lies in creating enough tritium to sustain fusion long enough to produce energy. But modeling the particle physics and chemical reactions involved in the tritium-creation process has proved beyond the capabilities of classical supercomputers.

In the new study, however, scientists say they have addressed this bottleneck by simulating nine molecular configurations of a liquid salt that contains fluorine, lithium and beryllium (FLiBe) — one of the leading candidate materials for extracting tritium.

This is the first time quantum computers have been used to model reactions inside a fusion reactor. If perfected, FLiBe could provide a near-limitless source of fuel for nuclear fusion reactors, they said, but the chemistry involved is incredibly complex.

Demystifying complex chemistry

A "blanket of molten salt" made of FLiBe is wrapped around the nuclear reaction inside a fusion reactor, IBM researchers told Live Science. This provides both a fuel source and a thermal shield for the device.

To create enough tritium, the researchers had to calculate the physics involved while a process called "neutron bombardment" constantly altered the blanket's chemistry. Designing a salt that holds up under competing demands and keeps releasing tritium is a key problem in building this kind of reactor.

"If tritium grabs onto fluorine in the salt, it forms tritium fluoride, which is corrosive and stubborn to remove," the researchers explained. "If it binds to another tritium atom to form a gas, it bubbles out on its own. Predicting which way the reaction goes means modeling the interaction between tritium and the salt with high precision and accuracy that is challenging for classical methods."

Because no ordinary computer can perform the necessary calculations, the team used a combination of AI running on the Frontier supercomputer at ORNL, alongside quantum computing algorithms running on an IBM Quantum Heron quantum processing unit (QPU) in New York. The resulting workflow demonstrated a proof of concept for offloading complex chemistry computations to a quantum computer.

That workflow relied on a technique called wave-function-based embedding, which fragments the calculation into easier-to-calculate clusters, the scientists said in the study. They used classical computers to solve the smaller clusters and passed off the more difficult chunks to a quantum computer. The classical computers then stitched the molecule back together.

This is a method that study co-author Kenneth Merz, a biochemist and principal investigator at Cleveland Clinic Research, pioneered in previous research. Earlier this year, in collaboration with IBM and the Japanese national research institute RIKEN, he used quantum computers to calculate the structure of a 12,635-atom protein.

Fusing quantum and AI

In the new study, the researchers tested their model against known molecular configurations that were already solved by a nonhybrid classical system and determined that the accuracy was maintained with the addition of quantum computations.

This proof of concept should serve as a direct pathway for scaling the models used to predict tritium production within fusion reactors, potentially solving what may be the biggest hurdle to large-scale fusion energy production.

The broader workflow the scientists outlined in a technical blog post involved three stages. First, AI agents proposed and screened many candidate salts from the ORNL database, and for each candidate, calculations estimated various qualities in the tritium breeding process, including how much fuel the salt would make under neutron bombardment.

The most promising salts then went to a supercomputer, which modeled them atom by atom, using the density functional theory (DFT) process to approximate how a molecule's electrons would arrange themselves. These are expensive simulations, so the scientists used "AI stand-ins" trained to reproduce the physics to run them fast enough to be useful. The third stage brought in the quantum computer to figure out where the tritium would bind, which is a shortcoming for DFT.

In the future, the research team will model larger molten-salt systems and study more molecular configurations before evaluating whether AI can slash the time it will take to find a promising molten-salt material.

The wider aim, the scientists told Live Science, is to build a reliable computational pathway for fusion-materials discovery that can help researchers predict how well a blanket material breeds tritium, whether that tritium can be recovered, and how the material may perform in the extreme environment of a fusion reactor.

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Researchers have created a new chip that turns one of quantum computing's biggest frailties into a programmable feature. They say this first-of-its-kind experiment could carry implications for developing error-corrected, fault-tolerant quantum computers in the future.

Unlike digital bits in a classical computer, which are represented as either "on" or "off," a quantum bit (qubit) has a much higher failure rate — roughly 1 in 1,000, compared with 1 in 1 billion for digital bits. That's because quantum computers are susceptible to "noise" — interference that's often cited as the biggest barrier preventing quantum computers from being more capable than the fastest supercomputers.

As engineers develop quantum systems that are large enough in scale to perform useful functions, the amount of noise generally increases. Scientists can combat this noise using various error-correction techniques. But despite recent progress in this field, the challenge of developing a truly fault-tolerant quantum computer remains.

That's because noise comes from various sources, many of which scientists have no control over. These include unpredictable disturbances in Earth's magnetic field, nearby radiation from Wi-Fi routers and other electronic devices, cosmic rays from space, and even neighboring qubits. This unpredictability has made it difficult to study this noise.

But researchers have now devised an experiment that turns the error-correction paradigm on its head. Instead of trying to rid a quantum system of noise, they have created a chip that lets them introduce errors at will so they can examine noise and signal loss in a controlled environment.

In the new study, published May 9 in the journal Nature Communications, the researchers described how this quantum computing chip uses photons captured from laser pulses as qubits. It also has what the researchers called a "side channel" that photons can be diverted to so the team could imitate the losses that occur under normal operating conditions and study them in detail.

"In many quantum experiments, anything that does not fit the ideal textbook picture is simply treated as loss and ignored," Govind Krishna, first author of the study and a doctoral student at the KTH Royal Institute of Technology in Sweden, said in a statement. "The chip enables us to simulate those non‑ideal processes in a controlled way."

The chip can be programmed to imitate errors in multiple ways, thus making it possible to simulate specific types of loss due to noise. The researchers can essentially modulate the amount of noise the system simulates in order to generate conditions for practical study. They do this by adjusting the number of photons that get sidetracked and the degree of quantum superposition, in which qubits share information over space and time through a process called quantum entanglement.

"The chip works a bit like a programmable railway junction for quantum light," Krishna explained. "By changing the control signals, we can decide whether the photons mostly stay on the main track, are mostly diverted to the loss channel, or end up in superpositions that depend on their quantum interference."

This means the noise itself becomes an asset that scientists can use to further improve quantum computing systems, rather than trying to eliminate it.

According to the study, the novel chip design can model errors in any type of quantum system — even a non-photonic system, like a superconducting qubit-based quantum computer or one designed with neutral atom qubits.

The scientists ultimately want to give researchers more tools to study how noise infiltrates and accumulates in quantum circuits. This could, in theory, lead to a greater understanding of how to perform more effective error-correction techniques in future systems, especially as those systems scale and interact with their environment even more.

"Understanding how quantum systems behave under this messiness is crucial if we want our experiments to say something about nature as it really is, not just idealized setups," Krishna said.

For the first time, scientists can develop computer chips with transistors smaller than 1 nanometer. The new "NanoStack" architecture that has made this possible could even one day lead to transistors as small as 0.1 nm, the scientists claimed.

The new 0.7 nm transistors are significantly smaller than those that feature in standard 2 nm semiconductor chips used in supercomputers, AI systems and advanced graphics processing units (GPUs). While size designation doesn't necessarily correlate with an exact measurement of the transistors on the chips, it does represent their general capabilities.

Essentially, the smaller the transistors and their supporting components, the more you can fit on a chip. A typical 2 nm chip design, for example, can fit roughly 50 billion transistors onto a space the size of a human fingernail.

The new chip features transistors that are so diminutive they're not measured in nanometers but "angstroms," a unit of measurement typically reserved for atoms. The first of these chips is expected to be manufactured with transistors that are a mere 7 angstroms — equivalent to 0.7 nanometers or roughly the width of a glucose molecule.

At this size, engineers can squeeze nearly 100 billion transistors into a fingernail-size space — nearly twice that of the current 2 nm platform.

Stacking and staggering

The scientists achieved this feat using a novel technique called "nanostacking," which they first outlined in a study published as part of the peer-reviewed 2025 Symposium on VLSI Technology and Circuits and uploaded July 2025 to the IEEE Xplore server. This enables engineers to vertically stack the nanosheets used to build the previous generation of 2 nm computer chips.

The technology used in all conventional circuits — known as complementary metal-oxide-semiconductor (CMOS) — demands extremely high temperatures during manufacturing. As transistors shrink, they also suffer from issues such as "charge trapping" — where electrons or holes become immobilized by defects or impurities — and "gate leakage" — static power dissipation.

Such problems have posed a challenge to attempts to shrink transistor size below 2 nm, and thus improve the performance and efficiency of computer chips beyond today's best capabilities. IBM's three-dimensional stacked architecture, however, aims to alleviate some of these pain points, the scientists said.

"NanoStack is nanosheets transistors stacking on top of each other. But it's not through a simple monolithic lithography and etch process," said Huiming Bu vice president for IBM semiconductors global R&D and Albany operations, during a press briefing.

"What happens here is we actually stack the device. I call it stacking, but also staggering. Stacking in vertical direction, so the front side of each transistor and the backside of each transistor can be contacted independently for signal and power. The stacking of these transistors are done by single dielectric bonding, which is a key innovation that we have developed."

IBM representatives added in the briefing that the new technology provides up to 50% greater performance with a 70% reduction in energy use versus the 2 nm platform — and will eventually replace this technology altogether within the next five years.

The scientists say the research could carry deep implications for the computing industry, with revolutionary impacts on the artificial intelligence (AI) and quantum computing sectors.

One of the immediate technological benefits could also lie in creating better static random access memory (SRAM) chips, which are used for a variety of computing applications, including CPU caching, networking and in devices such as pacemakers and vehicle sensors.

SRAM is also vital in AI processing because it's located close to processing cores (versus other kinds of RAM modules that are often separate components), increasing the speed of data shuttling around systems and therefore reducing bottlenecks.

IBM representatives added in the press briefing that they demonstrated a 40% improvement in the scaling of SRAM memory versus the 2 nm platform. This will be a boon to AI workflows, which demand much higher bandwidth and efficiency.

The future of computing

"We actually have entered a domain that semiconductor manufacturing is almost magic," Huiming added about the design process. "Think about the structure we are building here. We actually deposit the layer atom by atom, and we actually layer atom by atom."

IBM representatives said the nanostacking approach isn't a minor upgrade but a generational shift that will eventually enable foundries to scale these chips from 0.7 nm transistors all the way to a single angstrom or just 0.1 nm — keeping Moore's Law alive for a little longer at least.

Shrinking the transistor nodes on these chips will allow for more powerful processes, they said, thanks to a near-twice jump in the transistor count, while the stacked and staggered design significantly reduces the energy requirements. Huiming said that while everybody demands performance, nobody wants to pay the bill for the power.

"It will replace nanosheet as today's mainstream [platform] at leading foundries. Whether it's CPU or GPU," he added. "And we believe that transition will happen at around 7 angstroms. So within a decade, this will become another mainstream [platform] that we have invented. This is the next jump in technology."

The findings of the 2025 study suggest that not only can the chipset provide much-improved performance with much lower energy consumption, but it may also provide a path toward reducing the thermal impact that high-power computing has on hardware.

These innovations could also have an impact on quantum computing, IBM representatives said, as they could lead to improvements in the classical systems with which quantum computers will work together as the technology emerges.

"For quantum computing, we need to use lots of classical compute with it," Jay Gambetta, IBM's director of research, said during the press conference. "We want to build decoders, we want to build controllers for decoders and accelerators. And we are working right now on that type of classical with the 2 nm [platform]. If we can continue to change the platform, use more efficient, more powerful [chipsets], it will only help the rate and pace at which we've got to build the classical compute that goes along with the quantum."

Scientists say they have uploaded a real genome to a quantum computer for the first time, marking an important step in applying the emerging technology to biology.

The researchers encoded the entire genome of the hepatitis D virus (HDV) onto a system powered by IBM's 156-qubit Heron quantum processing unit. This achievement came during the Quantum for Bio (Q4Bio) challenge, a competitive international research program designed to accelerate quantum computing applications for human health. The goal was to demonstrate that quantum computers could handle real-world genomic data in a format the machines could actually process.

A genome is naturally stored as a long sequence of letters (A, C, G, and T/U), whereas a quantum computer works with quantum states represented by qubits. Simply copying DNA letters into qubits is not enough; the information has to be transformed into a quantum representation that can be prepared, manipulated, and measured by the hardware.

The scientists with the Wellcome Sanger Institute converted the HDV genome into a quantum-compatible format, allowing quantum algorithms to analyze genetic information rather than just theoretical problems.

They said in a statement that they specifically targeted the most complex and variable genomes ‪—‬ tasks that can exceed the current capabilities of classical computers, including artificial intelligence (AI) systems.

Where quantum computing and biology intersect

"When we work with pangenomes, the information is presented in a form of a tangled maze, but we are building quantum algorithms to help find the best path through this maze when regular tools, such as classic computers, just get hopelessly stuck," said leader of the research team, Sergii Strelchuk, an associate professor at the Department of Computer Science at the University of Oxford.

"We’re aiming for a simple but game-changing idea by bringing quantum computing into the world of genomics."

The same researchers already demonstrated four key genomics capabilities on real quantum hardware within the same Q4Bio genomics project. They used data encoding to convert DNA sequences into a quantum-compatible format.

A step called sequence alignment mapped DNA fragments into reference genomes, while a process called pangenome assembly built genomes from multiple individuals' DNA data. They also used, phylogenetic tree construction to map evolutionary relationships among organisms.

The scientists chose HDV because it has a compact genome and is clinically relevant. Although its RNA folds into intricate secondary structures — rather than existing as a simple linear sequence — and it mutates rapidly (like many RNA viruses), HDV has one of the smallest known animal virus genomes — roughly 1,700 nucleotides of circular RNA.

It causes severe blood-borne liver infections through contact with infected bodily fluids, making it an ideal test case that balances complexity with practical biomedical importance, the team said.

Increasingly complex computations

The work also demonstrates that pangenomes — collections of genome sequences from many individuals of the same species — are where quantum computing truly shines. As more genomes join a pangenome, conventional computing resources can be overwhelmed due to combinatorial growth in complexity.

A pangenome is not just a collection of genomes stored side by side but a data structure that captures all the genetic variation across many individuals, strains, or populations. As more genomes are added, the amount of variation that must be represented, compared, and indexed grows rapidly.

Quantum machines may be better able to navigate this computational complexity because they can represent and process many possible genetic patterns at once in a way that might make certain large-scale comparison and search problems in genomics faster (or more efficient) than traditional computers.

In the future, faster and more powerful genomic analysis could let scientists rapidly track infectious diseases, improve their understanding of rare genetic disorders, and pinpoint disease-causing mutations, the team said. Loading the hepatitis D genome onto a quantum computer opens the door to solving biological problems that have been impossible for classical computers to tackle, James McCafferty, chief information officer at the Wellcome Sanger Institute, said in the statement.

Although the accomplishment is promising, practical applications may still be years away, Strelchuk and colleagues on the Q4Bio team said in the statement. The team wants to package these capabilities into a usable service that would allow the wider scientific community to upload data and choose between classical or quantum approaches (or both) to address computational challenges.

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A Chinese company has unveiled what its researchers are calling the world’s first "dual-core" quantum computer. It's a neutral-atom system designed to improve stability, efficiency and error correction by pairing two independent qubit arrays in a single machine.

The device, called "Hanyuan-2," is being promoted as a step toward more scalable quantum hardware. The Wuhan-based company CAS Cold Atom Technology announced the new machine in May, according to reports by ST Daily, a Chinese state media outlet, with technical details published on its website.

Gui-Guo Ge, a senior solutions expert at CAS Cold Atom Technology, the company behind the dual-core computer, told ST Daily that the system is built on independently controllable neutral-atom array technology. It works by conjoining two quantum arrays comprising a total of 200 qubits made from rubidium atoms (100 rubidium-87 atoms and 100 rubidium-85 atoms).

Ge added that the two cores are both complete arrays that can operate in parallel to boost computational efficiency or work in a "one main core and one auxiliary core" configuration to create more stable logical bits. That design is intended to address long-standing technical bottlenecks in single-core systems, including limited expansion and interference between neighboring qubits.

The dual-core architecture matters because quantum computers are notoriously fragile. Qubits are prone to "noise" in the form of small disturbances such as temperature fluctuations or electromagnetic interference, which can disrupt calculations. By splitting the system into two cooperating cores, Hanyuan-2 aims to reduce those problems by allowing the cores to correct each other's errors and divide tasks between them.

The setup offers a modular path to scaling up quantum processing units (QPUs), and the use of neutral atoms affords several advantages. For one, neutral atoms don't require massive dilution refrigerators that cool components to near absolute zero to function the way superconducting quantum computers, like those in use at IBM or Google machines do, meaning lower energy requirements.

Because neutral atoms are electrically neutral, they interact less with their environment than many other types of qubits, meaning qubits can, in theory, preserve quantum information for longer, with less decoherence — when calculations fail due to the collapse of superposition — and potentially improved error rates, providing longer coherence times.

Hanyuan-2 includes more than 500 optical tweezers arrays and a qubit lifetime of 100 seconds, according to the report. It also uses a standard rack-mounted design and needs only a small laser-cooling setup with power consumption below 7 kilowatts. This means it can be deployed in ordinary environments rather than specialized cryogenic facilities.

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Researchers have created a new type of "quantum operation" that is dramatically more stable than previous methods. The achievement brings one hardware design, in particular — neutral‑atom qubits — a step closer to powering useful quantum computers.

Quantum computers use qubits that can exist in a state of 0, 1 or a superposition of both. Key to their processing power are "gates" capable of shuffling qubits between those states so they can run calculations in parallel. One critical type of gate is called a swap gate, which allows information to be routed through a machine by exchanging two qubits' states.

Many quantum systems rely on highly excited electronic states or collisions between atoms, as well as on the tunnel effect, in which particles slip through obstacles that would be impassable according to classical physics. However, swap gates that use those techniques (particularly the tunnel effect) are subject to how quickly lasers — which suspend neutrally charged atoms in place to form the qubits — can be turned on and how powerful they are.

This means that tiny fluctuations in the timing or strength of a laser could introduce errors and a lack of fidelity into the system, making a gate unreliable.

It feeds into the major bottleneck preventing scientists from scaling up quantum computing so they can be more powerful than the world's fastest supercomputers: qubits are highly susceptible to sustaining errors and breaking down during calculations. This rate is roughly 1 in 1,000 versus 1 in 1 trillion for conventional bits.

To resolve this issue, scientists at ETH Zurich devised a way to make qubits in neutral-atom quantum computers far more stable than ever before. They outlined their findings in a study published April 8 in the journal Nature.

Opening the gateway to more stable quantum computers

Rather than relying on conventional gates, the team used a subtler physical effect called a geometric phase. Unlike other methods for implementing quantum gates for neutral atoms or trapped particles, which depend on how fast and hard atoms are pushed, their swap gate exploits the path the atoms take through an artificial "crystal of light" built by intersecting laser beams (called an optical lattice).

Neutral‑atom platforms promise thousands of qubits in a single device. This setup uses tens of thousands of potassium atoms cooled to near absolute zero and held in place by laser light. Yann Hendrick Kiefer, a postdoctoral researcher at the ETH Zürich Institute for Quantum Electronics and first author of the study, told Live Science how this works.

"Laser light is nothing but monochromatic electromagnetic radiation," Kiefer said in an email. "If a neutral atom is placed inside this electric field a dipole moment is induced which leads to a force that enables us to hold atoms in place."

When two of those potassium atoms are brought close enough that their quantum waves overlap, their combined state changes in a way that depends only on the geometry of their motion, not on how quickly they move or how intense the lasers are. This makes the swap operation far less sensitive to experimental noise.

"Quantum mechanics is described by wave functions," Kiefer said. "Manipulation of this wavefunction generally introduces a phase on the wavefunction, which can be either of dynamical or geometric origin."

Dynamical quantum methods create this phase based on highly precise control over things like energy levels, timing, and laser strength, which means even tiny mistakes can cause errors. The geometric approach works differently: instead of depending on exact timing or force, it depends mainly on the overall path the system takes from start to finish. Because of that, it’s naturally less sensitive to outside disturbances or small imperfections, making these quantum operations more stable and reliable.

Building machines that will need far fewer qubits than we thought

Using this method, the research team achieved a very robust swap gate with a precision of better than 99.91%, operating in under a millisecond (one-thousandth of a second) across a system with a remarkable 17,000 qubit pairs. While some superconducting or trapped‑ion gates can be sub‑microsecond (one-millionth of a second), those systems typically run such gates on only a handful of qubit pairs at once.

The team also proved that they were capable of creating "half-swap" gates, which are critical for running real quantum algorithms. Half‑swap gates — a quantum operation that only swaps two qubits partway instead of completely — are vital because entanglement is the special ingredient in quantum computing. A full swap mostly just moves information around, but a half-swap can both partially exchange information, and create correlations between qubits that classical bits can't have. The scientists hope to eventually pair these robust swaps with a quantum gas microscope — which can image and target individual atom pairs — to build a more flexible, programmable quantum computing architecture.

That said, Kiefer admits a practical quantum computer is still way off. "Quantum computing on a practical scale still requires significant advancements," he said. "The most limiting factors are twofold: scale and fidelity."

However, Kiefer remains optimistic. He cited a recent study that explored how we could one day solve complex problems like Shor's algorithm with a system that uses as few as 10,000 qubits, rather than the millions we previously assumed we would need.

Shor's algorithm is a quantum recipe that can quickly crack certain kinds of modern encryption by finding the secret prime‑number ingredients of a big number faster than a classical computer can, and it remains a widely used benchmark in quantum computing research.

"There is a lot of work to be done before actually solving Shor's algorithm," Kiefer said, "but we are entering the phase in which the dream of quantum computing might actually be slowly converted into reality — exciting times!"

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Scientists have developed a new fabrication method for creating superconducting quantum bits (qubits) that could remain coherent for three times longer than current state-of-the-art systems in labs — allowing them to conduct more powerful quantum computing operations.

The new technique, described in a study published Nov. 5 in the journal Nature, relies on the use of a rare earth element called tantalum. This belongs to the "transition metals" group of the periodic table and is "grown" on minerals such as tantalite and silicon by building up a metallic film atom-by-atom.

Researchers used tantalum grown on silicon to create qubits capable of remaining coherent for up to 1.68 milliseconds. This is roughly three times longer than the coherence times reported in a lab setting, and up to 15 times longer than in the superconducting qubits used by the likes of Google and IBM in their quantum processing units (QPUs), the scientists said in a statement.

"The real challenge, the thing that stops us from having useful quantum computers today, is that you build a qubit and the information just doesn’t last very long," said Andrew Houck, Princeton’s dean of engineering and co-principal investigator of the study, in the study. "This is the next big jump forward."

Decoherence and imperfection

Coherence in quantum computing is a measure of how long a qubit can maintain its wave state. When qubits decohere, they lose information. This makes maintaining coherence one of the biggest challenges in quantum computing.

Scientists have spent some years trying to harness tantalum as a material to develop qubits. When a superconducting material such as tantalum is cooled to near absolute zero, circuits built within the material can operate with close to no resistance. This allows for faster quantum operations, but the speed and number of operations are fundamentally limited by how long qubits can maintain their information states.

An advantage of tantalum is that it’s easier to scrub free of contaminants that can lead to imperfections in the manufacturing process, where any irregularity can cause affected qubits to decohere faster. Tantalum’s inert resilience protects it from certain state changes related to corrosion and molecular displacement; it won’t even absorb acid when immersed. This makes it a perfect candidate for use as a superconducting material for quantum computing, the scientists said in the study.

But keeping the qubit material free from defects is only half the battle. The manufacture of a quantum processor requires both a base layer material and a substrate. In previous experiments, scientists achieved state-of-the-art quantum computing results using processors built with a tantalum base layer and a sapphire substrate. These experiments were successful, but coherence rates were still under one millisecond.

The Princeton team replaced the sapphire substrate used in those experiments with a high-resistivity silicon developed using proprietary techniques. According to the study, they achieved coherency rates as high as 1.68 milliseconds on systems as large as 48 qubits — marking an all-time best for superconducting qubits.

The new qubit design is similar to those used in superconducting quantum processors developed by leading companies such as Google and IBM. Houck even added that "swapping Princeton’s components into Google’s best quantum processor, called Willow, would enable it to work 1,000 times better."

What this means for the quantum computing industry remains unclear. While the scientists have progressed the coherence rates of qubits significantly, challenges remain. Chief among them is the availability of tantalum. As of 2025, tantalum is considered a scarce metal with most mining taking place in Africa.

While the new qubits significantly increase coherence, they still need to be tested at larger sizes using wafer-scale chipsets before they can be integrated with today’s commercially deployed quantum computers.

A U.K. startup has created the world's first silicon-based quantum computer manufactured using the same transistor technology found in nearly all modern digital electronics.

The machine is built using the complementary metal-oxide-semiconductor (CMOS) chip fabrication process — the same used to create the chips for devices like smartphones, laptops and digital cameras.

CMOS technology is so widely used because it produces chips that don't draw power when idle. Its integration in a quantum computer paves the way for broad adoption and less expensive manufacturing processes.

Another important element of the machine, built by the company Quantum Motion, is its relatively small footprint. The machine can be housed in just three 19-inch server racks, including the dilution refrigerator and integrated control electronics that manipulate the qubits and produce the extremely low temperatures required to maintain their fragile quantum states.

The system combines a quantum processing unit (QPU) with a user interface and industry-standard control software — the specialized layer that acts as the interpreter between a high-level quantum program (the algorithm) and the physical quantum hardware (the qubits), such as Qiskit and Cirq — to provide a complete quantum computing platform. It uses spin qubits — a type of qubit that encodes quantum information in the spin (intrinsic angular momentum) of an elementary particle, most commonly a single electron.

It's also highly scalable, Quantum Motion representatives said Sept. 15 in a statement. The QPU itself is based on tile architecture — a modular design approach where a processor or a system-on-a-chip (SoC) is built from smaller, self-contained and specialized units called tiles or chiplets.

The QPU condenses the necessary compute, readout and control elements into a single, dense array that can be deployed repeatedly on a single chip. This means that future iterations of the QPU, the physical hardware where quantum computation happens, can be upgraded to include millions of qubits, representatives said, and the system could allow future versions of the company's QPU to be easily swapped in for the existing processor.

“This is quantum computing’s silicon moment,” said James Palles‑Dimmock, CEO of Quantum Motion. “Today’s announcement demonstrates you can build a robust, functional quantum computer using the world’s most scalable technology, with the ability to be mass-produced.”

Related: Physicists force atoms into state of quantum 'hyper-entanglement' using tweezers made of laser light

Quantum Motion representatives say that this system is the first step to delivering commercially viable quantum computers within the decade.

The system is currently deployed at the U.K. National Quantum Computing Centre (NQCC) – a national lab for quantum computing, funded primarily through the UK Research and Innovation (UKRI) program. UKRI is a public body that directs research and innovation funding in the U.K.

Quantum Motion's system also represents the first silicon spin‑qubit computer developed under the auspices of NQCC’s Quantum Computing Testbed Programme, an initiative to build seven prototype quantum computers using differing technologies and test their viability.

The computer builds on research undertaken by Quantum Motion in conjunction with University College London (UCL) to create more fault-tolerant quantum systems. That research demonstrated 98% accuracy in two-qubit gates, the fundamental building block of a quantum circuit. That's a world-leading mark in qubits fabricated in natural silicon on a 300mm wafer scale, the same material used in the new computer.

Fault tolerance is critical to quantum computing because qubits are notoriously fragile and error-prone. The instability is due to a property called decoherence.

Superposition (the ability for a qubit to exist in multiple states at once) and entanglement (the ability of two or more qubits to be connected to one another and share the same state across any distance, so that altering one alters the other simultaneously), the keys to quantum computation, are both fragile states that can be destroyed by even the slightest interaction with the environment.

Changes in temperature, electromagnetic interference or other environmental factors can distort or collapse those properties, leading to inaccurate results. That fragility is one of the biggest obstacles to scalable and powerful quantum computing. That's why plenty of quantum computing research is in the area of quantum error correction (QEC).

As part of the SiQEC silicon quantum error correction project, Quantum Motion leverages silicon spin qubits created using standard 300 mm semiconductor manufacturing processes and its error correction research to build fault-tolerant architectures that could scale to the millions of qubits needed for quantum advantage.

The primary edge this kind of manufacturing holds over other processes is the commonality of the silicon manufacturing. Because the facilities, standards and techniques for effectively mass-producing these kinds of chips are already well-established, they can be produced more cheaply, quickly and at a greater scale than other, more specialized components.

Scientists have sent quantum signals over standard fiber-optic cables using the same connectivity that powers today’s web, in what could be a major step towards a working quantum internet.

In a study published Aug. 28 in the journal Science, researchers used a custom-built quantum chip to package quantum data alongside a standard optical signal and transmit them over commercial infrastructure.

The breakthrough marks the first time quantum data has been sent using Internet Protocol (IP), the same communications standard that underpins today's broadband networks. The results suggest that quantum communications could run on networks already in use, rather than needing dedicated infrastructure.

"Unlike earlier experiments that required isolated, lab-based setups or specialized infrastructure, this approach integrates quantum communication into real-world networks for the first time," senior study author Liang Feng, professor of materials science and electrical engineering at the University of Pennsylvania, told Live Science in an email.

"Our Q‑Chip enables control of quantum signals and classical signals, so they travel together over the same fiber‑optic cables, using standard internet protocols."

Why can't the internet send quantum data?

Quantum data is carried by qubits — the basic units of quantum information. Unlike classical computer bits, which are represented as either 0 or 1, qubits can exist in a superposition of both states.

Related: Scientists use quantum machine learning to create semiconductors for the first time – and it could transform how chips are made

Qubits can also become entangled, meaning the state of one is symbiotically linked to the state of another, no matter how far apart they are. These properties enable quantum computers to perform calculations far beyond the reach of conventional computers — in parallel rather than in sequence.

However, these same properties also make quantum data notoriously fragile. Quantum states collapse when they're observed or measured, making quantum information extremely difficult to work with. In classical internet, traffic is directed by routers that read and interpret information as it moves through the network. This can't be done with quantum particles without destroying the very data being transmitted,because the superposition collapses as soon as it is observed.

How the Q-Chip works

The Q‑Chip, which stands for "quantum-classical hybrid internet by photonics", tackles this challenge by pairing each quantum signal with a classical "header" — a data packet containing routing and timing information that’s encoded into a fiber-optic laser pulse.

As this information travels through the network, it’s inspected by routers — devices that direct internet traffic by reading packet information and forwarding it to the correct destination. Routers use the header to determine where the data should go and how to get it there.

By timing the classical and quantum signals to travel together in a synchronized pulse, the chip enables routers to read the header's navigation information without interacting with or disrupting the quantum signal. This enables both to travel together via standard IP protocols.

While researchers have previously demonstrated that quantum data can be transmitted over standard fiber-optic cables, including alongside classical data in the same wavelength band, this latest study marks the first time that quantum signals have been transmitted using standard IP on live, real-world infrastructure.

This is crucial because it avoids the need for a separate quantum-only network, significantly lowering the barrier to deploying and scaling a quantum internet, said Feng.

"Using standard IP protocols means the Q‑Chip allows quantum communication to be managed like regular internet traffic with the already-developed tools for routing, addressing and coordination," he told Live Science.

"By attaching classical 'headers'" to quantum data, the Q‑Chip can route and manage quantum signals using the developed classical photonic devices, systems and infrastructure without disturbing the delicate quantum states, making this the first practical demonstration of quantum communication that fits within existing internet architecture."

To test the system, the team built a simple connection between a server and a receiver node, using a 1-kilometer (0.6 miles) stretch of commercial fiber operated by telecommunications company Verizon.

Because the classical header and quantum signal respond to interference from the environment in similar ways, the team could use the classical signal to correct for noise without disturbing the quantum state. This ensured the data reached its destination intact.

While the pilot setup was small, the researchers believe it marks a foundational step toward a full-scale quantum internet that could link quantum devices — particularly as the Q-Chip is made of silicon and fabricated using existing processes, meaning it can be mass-produced.

"In the next 5-10 years, the early stages of a quantum internet will likely focus on local networks and/or metro-scale quantum internet," Feng told Live Science. "Applications [could include] secure communication, interconnecting quantum computers and distributed quantum sensing such as ultra-precise navigation or timing."

Scientists have achieved the lowest quantum computing error rate ever recorded — an important step in solving the fundamental challenges on the way to practical, utility-scale quantum computers.

In research published June 12 in the journal APS Physical Review Letters, the scientists demonstrated a quantum error rate of 0.000015%, which equates to one error per 6.7 million operations.

This achievement represents an improvement of nearly an order of magnitude in both fidelity and speed over the previous record of approximately one error for every 1 million operations — achieved by the same team in 2014.

The prevalence of errors, or "noise," in quantum operations can render a quantum computer's outputs useless.

This noise comes from a variety of sources, including imperfections in the control methods (essentially, problems with the computer's architecture and algorithms) and the laws of physics. That's why considerable efforts have gone into quantum error correction.

While errors related to natural law, such as decoherence (the natural decay of the quantum state) and leakage (the qubit state leaking out of the computational subspace), can be reduced only within those laws, the team's progress was achieved by reducing the noise generated by the computer's architecture and control methods to almost zero.

Related: Scientists make 'magic state' breakthrough after 20 years — without it, quantum computers can never be truly useful

"By drastically reducing the chance of error, this work significantly reduces the infrastructure required for error correction, opening the way for future quantum computers to be smaller, faster, and more efficient," Molly Smith, a graduate student in physics at the University of Oxford and co-lead author of the study, said in a statement. "Precise control of qubits will also be useful for other quantum technologies such as clocks and quantum sensors."

Record-low quantum computing error rates

The quantum computer used in the team's experiment relied on a bespoke platform that eschews the more common architecture that uses photons as qubits — the quantum equivalent of computer bits — for qubits made of "trapped ions."

The study was also conducted at room temperature, which the researchers said simplifies the setup required to integrate this technology into a working quantum computer.

Whereas most quantum systems either deploy superconducting circuits that rely on "quantum dots" or employ the use of lasers — often called "optical tweezers" — to hold a single photon in place for operation as a qubit, the team used microwaves to trap a series of calcium-43 ions in place.

With this approach, the ions are placed into a hyperfine "atomic clock" state. According to the study, this technique allowed the researchers to create more "quantum gates," which are analogous to the number of “quantum operations” a computer can perform, with greater precision than the photon-based methods allowed.

Once the ions were placed into a hyperfine atomic clock state, the researchers calibrated the ions via an automated control procedure that regularly corrected them for amplitude and frequency drift caused by the microwave control method.

In other words, the researchers developed an algorithm to detect and correct the noise produced by the microwaves used to trap the ions. By removing this noise, the team could then conduct quantum operations with their system at or near the lowest error rate physically possible.

Using this method, it is now possible to develop quantum computers that are capable of conducting single-gate operations (those conducted with a single qubit gate as opposed to a gate requiring multiple qubits) with nearly zero errors at large scales.

This could lead to more efficient quantum computers in general and, per the study, achieves a new state-of-the-art single-qubit gate error and the breakdown of all known sources of error, thus accounting for most errors produced in single-gate operations.

This means engineers who build quantum computers with the trapped-ion architecture and developers who create the algorithms that run on them won't have to dedicate as many qubits to the sole purpose of error correction.

By reducing the error, the new method reduces the number of qubits required and the cost and size of the quantum computer itself, the researchers said in the statement.

This isn't a panacea for the industry, however, as many quantum algorithms require multigate qubits functioning alongside or formed from single-gate qubits to perform computations beyond rudimentary functions. The error rate in two-qubit gate functions is still roughly 1 in 2,000.

While this study represents an important step toward practical, utility-scale quantum computing, it doesn't address all of the "noise" problems inherent in complex multigate qubit systems.

In a world first, scientists have demonstrated an enigmatic phenomenon in quantum computing that could pave the way for fault-tolerant machines that are far more powerful than any supercomputer.

The process, called "magic state distillation," was first proposed 20 years ago, but its use in logical qubits has eluded scientists ever since. It has long been considered crucial for producing the high-quality resources, known as "magic states," needed to fulfill the full potential of quantum computers.

Magic states are quantum states prepared in advance, which are then consumed as resources by the most complex quantum algorithms. Without these resources, quantum computers cannot tap into the strange laws of quantum mechanics to process information in parallel.

Magic state distillation, meanwhile, is a filtering process by which the highest quality magic states are "purified" so they can be utilized by the most complex quantum algorithms.

This process has so far been possible on plain, error-prone physical qubits but not on logical qubits — groups of physical qubits that share the same data and are configured to detect and correct the errors that frequently disrupt quantum computing operations.

Because magic state distillation in logical qubits has not so far been possible, quantum computers that use logical qubits have not been theoretically able to outpace classical machines.

Related: What is quantum superposition and what does it mean for quantum computing?

Now, however, scientists with QuEra say they have demonstrated magic state distillation in practice for the first time on logical qubits. They outlined their findings in a new study published July 14 in the journal Nature.

"Quantum computers would not be able to fulfill their promise without this process of magic state distillation. It's a required milestone." Yuval Boger, chief commercial officer at QuEra, told Live Science in an interview. Boger was not personally involved in the research.

The path to fault-tolerant quantum computing

Quantum computers use qubits as their building blocks, and they use quantum logic — the set of rules and operations that govern how quantum information is processed — to run algorithms and process data. But the challenge is running incredibly complex algorithms while maintaining incredibly low error rates.

The trouble is that physical qubits are inherently "noisy," which means calculations are often disrupted by factors like temperature changes and electromagnetic radiation. That's why so much research has centered on quantum error correction (QEC).

Reducing errors — which occur at a rate of 1 in 1,000 in qubits versus 1 in 1 million, million in conventional bits — prevents disruptions and enables calculations to happen at pace. That's where logical qubits come in.

"For quantum computers to be useful, they need to run fairly long and sophisticated calculations. If the error rate is too high, then this calculation quickly turns into mush or to useless data," study lead author of the study Sergio Cantu, vice president of quantum systems at QuEra, told Live Science in an interview. "The entire goal of error correction is to lower this error rate so you could do a million calculations safely."

Logical qubits are collections of entangled physical qubits that share the same information and are based on the principle of redundancy. If one or more physical qubits in a logical qubit fail, the calculation isn't disrupted because the information exists elsewhere.

But logical qubits are extremely limited, the scientists said, because the error-correction codes applied to them can only run "Clifford gates" — basic operations in quantum circuits. These operations are foundational to quantum circuits, but they're so basic that they can be simulated on any supercomputer.

Only by tapping into high-quality magic states can scientists run "non-Clifford gates" and engage in true parallel processing. But generating these is extremely resource-intensive and expensive, and has thus far been unachievable in logical qubits.

In essence, relying on magic state distillation in physical qubits alone would never lead to quantum advantage. For that, we need to distill magic states in logical qubits directly.

Magic states pave the way for capabilities beyond supercomputing

"Magic states allow us to expand the number and the type of operations that we can do. So practically, any quantum algorithm that's of value would require magic states," Cantu said.

Generating magic states in physical qubits, as we have been doing, is a mixed bag — there are low-quality and high-quality magic states — and they need to be refined. Only then, can they fuel the most powerful programs and quantum algorithms.

In the study, using the Gemini neutral-atom quantum computer, the scientists distilled five imperfect magic states into a single, cleaner magic state. They performed this separately on a Distance-3 and a Distance-5 logical qubit, demonstrating that it scales with the quality of the logical qubit.

"A greater distance means better logical qubits. A Distance-2, for instance, means that you can detect an error but not correct it. Distance-3 means that you can detect and correct a single error. Distance-5 would mean that you can detect and correct up to two errors, and so on, and so on," Boger explained. "So the greater the distance, the higher fidelity of the qubit is — and we liken it to distilling crude oil into a jet fuel."

As a result of the distillation process, the fidelity of the final magic state exceeded that of any input. This proved that fault-tolerant magic state distillation worked in practice, the scientists said. This means that a quantum computer that uses both logical qubits and high-quality magic states to run non-Clifford gates is now possible.

"We're seeing sort of a shift from a few years ago," Boger said. "The challenge was: can quantum computers be built at all? Then it wasL can errors be detected and corrected? Us and Google and others have shown that, yes, that can be done. Now it's about: can we make these computers truly useful? And to make one computer truly useful, other than making them larger, you want them to be able to run programs that cannot be simulated on classical computers."

Scientists have demonstrated that a photonic qubit — a quantum bit powered by a particle of light — can detect and correct its own errors while running at room temperature. They say it is a foundational step toward scalable quantum processors.

In a new study published June 4 in the journal Nature, researchers at Canadian quantum computing startup Xanadu created a so-called "Gottesman–Kitaev–Preskill" (GKP) state directly on a silicon chip.

GKP states are a type of quantum state that spreads information across multiple photons in a pattern that enables small errors to be spotted and corrected. This means that each qubit is capable of correcting itself, without needing to be bundled into large arrays of redundant qubits — a common requirement in today’s error-correction methods.

It marks the first time this type of error-resistant quantum state has been generated using a process compatible with conventional chip manufacturing, the scientists said.

The breakthrough suggests that error-correcting quantum states could be produced with the same tools used to manufacture conventional computer chips — bringing reliable, room-temperature quantum hardware a step closer to reality.

The qubit-cooling conundrum

Quantum computers work very differently from the classical machines we use today. Classical computers store information in binary bits, represented as either 1s or 0s. Quantum systems, meanwhile, use qubits that can exist in a "superposition" of both states. This enables them to solve complex calculations in parallel, and they can one day perform far beyond the reach of conventional systems.

But qubits are notoriously fragile. Even the smallest fluctuations in temperature, electromagnetic radiation or environmental noise can disrupt a qubit’s state and corrupt its data.

To guard against this, many quantum systems operate at temperatures close to absolute zero (minus 459.67 degrees Fahrenheit or minus 273.15 degrees Celsius) using complex cooling systems to maintain "coherence" — the fragile quantum connection through which qubits perform calculations.

Related: Coldest-ever qubits could lead to faster quantum computers

While this cooling helps preserve quantum information, it also makes quantum computers bulky, expensive and impractical to scale. Xanadu’s solution seeks to address this by using photons — particles of light that don’t require deep cooling — to build qubits that run on silicon chips at room temperature.

The team’s GKP demonstration tackles another key challenge: quantum error correction. Most quantum systems today rely on groupings of multiple physical qubits that work together to detect and fix errors, known as a "logical qubit." Xanadu’s photonic qubit sidesteps this by handling correction within each individual qubit, simplifying the hardware and paving the way for more scalable designs.

"GKP states are, in a sense, the optimal photonic qubit, since they enable logic gates and error correction at room temperature and using relatively straightforward, deterministic operations," Zachary Vernon, CTO of hardware at Xanadu, said in a statement.

"This demonstration is an important empirical milestone showing our recent successes in loss reduction and performance improvement across chip fabrication, component design and detector efficiency."

The result builds on Xanadu’s earlier development of Aurora, a modular quantum computing platform that connects multiple photonic chips using optical fiber. While Aurora addressed the challenge of scaling across a network, this new chip focuses on making each qubit more robust — a critical requirement for building fault-tolerant systems.

Xanadu representatives said the next challenge was reducing optical loss, which happens when photons are scattered or absorbed as they travel through the chip’s components.

Scientists say they have made a breakthrough after developing a quantum computing technique to run machine learning algorithms that outperform state-of-the-art classical computers.

The researchers revealed their findings in a study published June 2 in the journal Nature Photonics.

The scientists used a method that relies on a quantum photonic circuit and a bespoke machine learning algorithm.

Using only two photons, the team's technique successfully demonstrated increased speed, accuracy and efficiency over standard classical computing methods for running machine learning algorithms.

The scientists say this is one of the first times quantum machine learning has been used for real-world problems and provides benefits that cannot be simulated using binary computers. Furthermore, due to its novel architecture, it could be applied to quantum computing systems featuring only a single qubit, they said.

Unlike many existing methods for achieving speedup through hybrid quantum-classical computing techniques, this new method doesn't require entangled gates. Instead, it relies on photon injection.

Related: 'The science is solved': IBM to build monster 10,000-qubit quantum computer by 2029

Essentially, the team used a femtosecond laser — a laser that emits light in extremely short pulses measured in femtoseconds (10⁻¹⁵ seconds) to write on a borosilicate glass substrate to classify data points from a dataset. The photons were then injected in six distinct configurations, which were processed by a hybrid quantum-binary system.

The scientists determined where the photonic measurements outperformed those conducted via classical computing by measuring how long it took the photons to complete the quantum circuit. They then isolated the processes where quantum processing provided benefit and compared the results to the classical outputs.

The researchers found that experiments run using the photonic quantum circuit were faster, more accurate and more energy-efficient than those conducted using only classical computing techniques. This boosted performance applies to a special class of machine learning called "kernel-based machine learning" that can have myriad applications across data sorting.

While deep neural networks have become an increasingly popular alternative to kernel methods for machine learning over the past decade, kernel-based systems have seen a resurgence in the past few years due to their relative simplicity and advantages when working with small datasets.

The team's experiment could lead to more efficient algorithms in the fields of natural language processing and other supervised learning models.

Perhaps most importantly, the study showcases a novel method for identifying tasks that quantum computers excel at in hybrid computer systems.

The researchers say the techniques used are scalable, meaning they could lead to even better performance as the number of photons or qubits increases. This could, in turn, make it possible to develop machine learning systems capable of exceeding the limits of today's models, which increasingly face power consumption limitations due to the massive energy requirements needed to process data via electronics.

The researchers claim their techniques will "open the door to hybrid methods in which photonic processors are used to enhance the performance of standard machine learning methods."

Scientists have developed a new type of computer chip that removes a major obstacle to practical quantum computers, making it possible for the first time to place millions of qubits and their control systems on the same device.

The new control chip operates at cryogenic temperatures close to absolute zero (about minus 459.67 degrees Fahrenheit, or minus 273.15 degrees Celsius) and, crucially, can be placed close to qubits without disrupting their quantum state.

"This result has been more than a decade in the making, building up the know-how to design electronic systems that dissipate tiny amounts of power and operate near absolute zero," lead researcher David Reilly, professor at the University of Sydney Nano Institute and School of Physics, said in a statement.

The scientists described the result as a "vital proof of principle" for integrating quantum and classical components in the same chip — a major step toward the kind of practical, scalable processors needed to make quantum computing a reality. The researchers published their findings June 25 in the journal Nature.

Qubits are the quantum equivalent of binary bits found in today's classical computers. However, where a classical bit can represent either 0 or 1, a qubit can exist in a "superposition" of both states. This enables quantum computers to perform multiple calculations in parallel, making them capable of solving problems far beyond the reach of today's computers.

Related: Quantum computers that are actually useful 1 step closer thanks to new silicon processor that could pack millions of qubits

Spin qubits, a type of qubit that encodes information in the spin state of an electron, have piqued the interest of scientists because they can be built using complementary metal-oxide-semiconductor (CMOS) technology.

This is the same process used to fabricate the chips found inside modern smartphones and PCs. In theory, this makes spin qubits much easier to produce at scale as it slips into normal manufacturing methods.

Other quantum computers use different types of qubits, including superconducting, photonic or trapped-ion qubits. But unlike these other types, spin qubits can be made on a massive scale using existing equipment.

However, spin qubits need to be kept at temperatures below 1 kelvin (just above absolute zero) to preserve "coherence." This is a qubit’s ability to maintain superposition and entanglement over time, and what is needed to unlock the parallel processing power that makes quantum computing so promising. Spin qubits also need electronic equipment to measure and control their activity.

"This will take us from the realm of quantum computers being fascinating laboratory machines to the stage where we can start discovering the real-world problems that these devices can solve for humanity," Reilly added.

The road to a single million-qubit chip

Integrating the electronics required to control and measure spin qubits has long posed a challenge, as even small amounts of heat or electrical interference can disrupt the qubits' fragile quantum state.

But this new, custom CMOS chip is designed to operate in cryogenic environments and at ultra-low power levels, meaning it can be integrated onto a chip alongside qubits without introducing thermal or electrical noise that would otherwise interrupt coherence.

In tests, the researchers ran single-gate and two-qubit gate operations with the control chip positioned less than 1 millimeter (0.04 inches) from the qubits. The control chip introduced no measurable electrical noise and caused no drop in accuracy, stability or coherence, the researchers said.

Additionally, the control chip consumed just 10 microwatts (0.00001 watts) of power in total, with the analogue components — used to control the qubits with electrical pulses — using 20 nanowatts (0.00000002 watts) per megahertz.

"This validates the hope that indeed qubits can be controlled at scale by integrating complex electronics at cryogenic temperatures," Reilly said.

"This will take us from the realm of quantum computers being fascinating laboratory machines to the stage where we can start discovering the real-world problems that these devices can solve for humanity," he added.

"We see many further diverse uses for this technology, spanning near-term sensing systems to the data centres of the future."

The findings could prompt more researchers to explore the power of spin qubits.

"Now that we have shown that milli-kelvin control does not degrade the performance of single- and two-qubit quantum gates, we expect many will follow our lead," study co-author Kushal Das, senior hardware engineer at Emergence Quantum and a researcher at the University of Sydney who designed the chip, said in the statement.

"Fortunately for us, this is not so easy but requires years to build up the know-how and expertise to design low-noise cryogenic electronics that need only tiny amounts of power."

Quantum computers capable of outperforming today’s fastest supercomputers may not need to be as large or power-hungry as we thought, researchers at Canadian startup Nord Quantique say.

The company has built a quantum bit (qubit) with built-in error correction, eliminating the need for the large clusters of physical qubits typically required for fault-tolerant quantum computing.

Nord Quantique plans to scale this design into a 1,000-logical-qubit machine by 2031. The system would be compact enough to fit inside a data center and require far less energy than current platforms, researchers said.

The announcement follows a 2024 milestone in which the company demonstrated a working prototype of its "bosonic qubit" — a device that integrates quantum error correction directly into its hardware. In a statement, Nord Quantique representatives described the new architecture as "a first in applied physics" and a practical route toward scalable, utility-grade quantum machines. The breakthrough addresses a longstanding challenge in quantum computing: maintaining the integrity of quantum information over time.

Quantum bits are extremely sensitive to heat, vibration and electromagnetic interference — even when cooled close to absolute zero (–460°F, or –273°C). Most quantum platforms address this using quantum error correction, which combines many physical qubits to form a single logical unit capable of absorbing and correcting errors through redundancy, so that any single failure doesn’t scrub the entire calculation.

Related: Quantum computers are here — but why do we need them and what will they be used for?

However, creating a single logical qubit traditionally requires dozens or even hundreds of physical qubits, significantly increasing the size, complexity and energy cost of a quantum computer. Nord Quantique’s system avoids this by using a single physical component to perform the role of a logical qubit.

Quantum computing in safe mode

At the core of the design is a superconducting aluminum cavity known as a bosonic resonator, cooled to near absolute zero. This cavity contains light particles (photons) that store quantum information in specific electromagnetic patterns formed within the resonator. These patterns, known as "modes," each represent a different way the field resonates inside the cavity, allowing the same quantum state to be encoded in parallel.

By distributing information across multiple modes within the same physical structure, the qubit can identify and correct certain types of interference. If one mode is disrupted, the others provide enough context to restore the correct state. This method, known as multimode encoding, gives each qubit internal fault tolerance, reducing the need for external error correction and enabling a 1:1 ratio between physical and logical qubits.

The researchers estimated that a 1,000-logical-qubit machine built on this architecture would occupy just 215 square feet (20 square meters) and consume only a fraction of the energy used by high-performance systems today.

They also calculated that a quantum computer built using their architecture could break a 830-bit RSA encryption key in an hour, consuming just 120 kilowatt-hours of energy. By comparison, a supercomputer would require nine days and 280,000 kilowatt-hours to solve the same problem, they said.

"The amount of physical qubits dedicated to quantum error correction has always presented a major challenge for our industry," Julien Camirand Lemyre, chief executive at Nord Quantique, said in a statement. "Multimode encoding allows us to build quantum computers with excellent error correction capabilities, but without the impediment of all those physical qubits."

To make the system more fault-tolerant, the researchers used a "bosonic code" called Tesseract code. This helps guard against common quantum faults such as bit flips, phase flips, control errors and leakage, where the qubit slips into a state that isn’t part of the system used to store and process information. Leakage is hard to correct because most error correction techniques only work inside the expected set of quantum states and can’t spot when something falls outside it.

To test the system’s reliability, the researchers ran repeated rounds of error correction and filtered out results where the qubit didn’t behave as intended.

About 12.6% of runs were filtered out, they said. In the remaining data, the qubit held its state through 32 rounds of error correction without measurable decay, suggesting that multimode encoding can preserve quantum information reliably under stable conditions.

Nord Quantique plans to release a 100-logical-qubit machine by 2029, with the full 1,000-qubit system scheduled for 2031. "Beyond their smaller and more practical size, our machines will also consume a fraction of the energy,” said Camirand Lemyre. "That makes them especially appealing to [high-performance computing] HPC centers where energy costs are top of mind."

Computer scientists say they’ve cracked the science behind error-correction in quantum computers thanks to new "4D codes."

Developed by Microsoft, the new codes were revealed in a blog post published June 19 and purport to address the problem of fault tolerance — arguably quantum computing’s biggest bottleneck.

All computers can produce errors. In classical computing, error correction is achieved by making multiple copies of every bit of information that’s sent. If one or more bits are lost or corrupted, the remaining bits still contain the original information.

Qubits, however, can’t be copied. They also cannot be measured without experiencing what’s called "collapse." This makes it much more challenging to detect and mitigate errors (which occur at a significantly higher rate than in classical bits) as they happen.

A typical quantum error-correction setup involves the addition of extra "physical" qubits to a system. These qubits are entangled with the "logical" qubits that typically carry quantum information. Instead of measuring the logical qubits, thus causing this collapse, scientists can check for errors by measuring the entangled physical qubits. This allows the computation process to continue.

Scientists typically employ 4D codes in the quantum error-correction process by recreating the topology of quantum processing surfaces on a four-dimensional lattice. This creates a self-correcting form of quantum memory.

Related: 'The science is solved': IBM to build monster 10,000-qubit quantum computer by 2029

The trouble is that most current error-correction techniques are either difficult to scale, resource-intensive, or both. The more physical qubits required to provide fault tolerance for a quantum system, and the more error-correction passes needed, the more energy is required for computation.

"Microsoft’s novel four-dimensional geometric codes require very few physical qubits per logical qubit, can check for errors in a single shot, and exhibit a 1,000-fold reduction in error rates," said technical fellow of advanced quantum development at Microsoft Quantum, Krysta Svore, in the blog post.

A twist to quantum error-correction

The findings, uploaded June 18 to the arXiv preprint database, center on putting a literal twist on the torus-shaped 4D geometric code used for error-correction in certain quantum computing systems.

The scientists developed geometric code that could be overlaid in a system to detect errors using a four-dimensional topography. This 4D code connects the sample space (where the correction codes run) to the operational space (where the qubits contain information) via entanglement.

It works in four dimensions using a mathematical expression that, essentially, allows entanglement points to make connections over the surface of a "torus," which can be imagined as a donut shape.

While 4D codes have been used to create self-correcting quantum memory in the past, their use here is considered novel because the researchers calculated a "twist" in the geometry that allows the same amount of code to cover the same amount of system space using fewer physical qubit entanglements.

By "twisting" the geometry, the 4D code overlay creates a larger representational space that reflects a greater portion of the quantum state of the actual qubits in use. Doing so allows researchers to detect errors in the code without disturbing the actual quantum processes occurring within the system.

The researchers ran their new “twisted” code on existing quantum computers and experimentally confirmed their theories in a separate preprint paper, published to the arXiv preprint server on June 13. Neither paper has yet been peer reviewed.

"Universal fault-tolerant quantum computers may be realized using 4D geometric codes, which are designed to enable efficiently realizing an increasing number of logical qubits with a modest number of physical qubits, while enabling low-depth logical cycles and universal fault tolerance," the scientists said in the study.

Furthermore, the researchers purportedly demonstrated a groundbreaking technique for "replacing" the atoms used as qubits when they’re lost. In certain quantum computing systems, qubits are created by snagging neutral atoms with laser tweezers and trapping them in place. During computations, these atoms can be lost or dropped.

The researchers say they could replace lost atoms mid-cycle using an atomic beam to force new atoms into the array without disrupting the calculations — a first, the scientists said in the study.

Based on the findings, the new 4D code family could represent the second breakthrough in quantum error-correction in as many weeks. On June 10, IBM made a similar statement when it announced that it had developed quantum error-correction techniques that will lead to the development of a demonstrably useful quantum computer by 2029.

Where IBM’s new method utilizes a top-down development approach that takes advantage of its bespoke hardware, Microsoft’s is built from the bottom up to address fault tolerance using an approach that may have other applications beyond the hardware and use-cases it was tested on.

IBM scientists say they have solved the biggest bottleneck in quantum computing and plan to launch the world's first large-scale, fault-tolerant machine by 2029.

The new research demonstrates new error-correction techniques that the scientists say will lead to a system 20,000 times more powerful than any quantum computer in existence today.

In two new studies uploaded June 2 and June 3 to the preprint arXiv server, the researchers revealed new error mitigation and correction techniques that sufficiently handle these errors and allow for the scaling of hardware nine times more efficiently than previously possible.

The new system, called "Starling," will use 200 logical qubits — made up of roughly 10,000 physical qubits. This will be followed by a machine called "Blue Jay," which will use 2,000 logical qubits, in 2033.

The new research, which has not yet been peer-reviewed, describes IBM's quantum low-density parity check (LDPC) codes — a novel fault-tolerance paradigm that researchers say will allow quantum computer hardware to scale beyond previous limitations.

"The science has been solved" for expanded fault-tolerant quantum computing, Jay Gambetta, IBM vice president of quantum operations, told Live Science. This means that scaling up quantum computers is now just an engineering challenge, rather than a scientific hurdle, Gambetta added.

Related: Google's 'Willow' quantum chip has solved a problem that would have taken the best supercomputer a quadrillion times the age of the universe to crack

While quantum computers exist today, they're only capable of outpacing classical computer systems (those using binary calculations) on bespoke problems that are designed only to test their potential.

One of the largest hurdles to quantum supremacy, or quantum advantage, has been in scaling up quantum processing units (QPUs).

As scientists add more qubits to processors, the errors in calculations performed by QPUs add up. This is because qubits are inherently "noisy" and errors occur more frequently than in classical bits. For this reason, research in the field has largely centered on quantum error-correction (QEC).

The road to fault tolerance

Error correction is a foundational challenge for all computing systems. In classical computers, binary bits can accidentally flip from a one to a zero and vice versa. These errors can compound and render calculations incomplete or cause them to fail entirely.

The qubits used to conduct quantum calculations are far more susceptible to errors than their classical counterparts due to the added complexity of quantum mechanics. Unlike binary bits, qubits carry extra "phase information."

While this enables them to perform computations using quantum information, it also makes the task of error correction much more difficult.

Until now, scientists were unsure exactly how to scale quantum computers from the few hundred qubits used by today's models to the hundreds of millions they theoretically need to make them generally useful.

But the development of LDPC and its successful application across existing systems is the catalyst for change, Gambetta said.

LDPC codes use a set of checks to detect and correct errors. This results in individual qubits being involved in fewer checks and each check involving fewer qubits than previous paradigms.

The key advantage of this approach is a significantly improved "encoding rate," which is the ratio of logical qubits to the physical qubits needed to protect them. By using LDPC codes, IBM aims to dramatically reduce the number of physical qubits required to scale up systems.

The new method is about 90% faster at conducting error-mitigation than all previous techniques, based on IBM research. IBM will incorporate this technology into its Loon QPU architecture, which is the successor to the Heron architecture used by its current quantum computers.

Moving from error-mitigation to error-correction

Starling is expected to be capable of 100 million quantum operations using 200 logical qubits. IBM representatives said this was roughly equivalent to 10,000 physical qubits. Blue Jay will theoretically be capable of 1 billion quantum operations using its 2,000 logical qubits.

Current models have about 5,000 gates (analogous to 5,000 quantum operations) using 156 logical qubits. The leap from 5,000 operations to 100 million will only be possible through technologies like LDPC, IBM representatives said in a statement. Other technologies, including those used by companies like Google, will not scale to the larger sizes needed to reach fault tolerance, they added.

To take full advantage of Starling in 2029 and Blue Jay in 2033, IBM needs algorithms and programs built for quantum computers, Gambetta said. To help researchers prepare for future systems, IBM recently launched Qiskit 2.0, an open-source development kit for running quantum circuits using IBM's hardware.

"The goal is to move from error mitigation to error correction," Blake Johnson, IBM's quantum engine lead, told Live Science, adding that "quantum computing has grown from a field where researchers are exploring a playground of quantum hardware to a place where we have these utility-scale quantum computing tools available."

Quantum computing is expected to leave classical computing in the dust when it comes to solving some of the world’s most fiendishly difficult problems. The best quantum machines today have one major weakness, however — they are incredibly error-prone.

That’s why the field is racing to develop and implement quantum error-correction (QEC) schemes to alleviate the technology’s inherent unreliability. These approaches involve building redundancies into the way that information is encoded in the qubits of quantum computers, so that if a few errors creep into calculations, the entire computation isn't derailed. Without any additional error correction, the error rate in qubits is roughly 1 in 1,000 versus 1 in 1 million million in classical computing bits.

The unusual properties of quantum mechanics make this considerably more complicated than error correction in classical systems, though. Implementing these techniques at a practical scale will also require quantum computers that are much larger than today’s leading devices.

But the field has seen significant progress in recent years, culminating in a landmark result from Google’s quantum computing team last December. The company unveiled a new quantum processor called Willow that provided the first conclusive evidence that QEC can scale up to the large device sizes needed to solve practical problems.

"Its a landmark result in that it shows for the first time that QEC actually works," Joschka Roffe, an innovation fellow at The University of Edinburgh and author of a 2019 study into quantum error correction, told Live Science. "There's still a long way to go, but this is kind of the first step, a proof of concept."

Why do we need quantum error correction?

Quantum computers can harness exotic quantum phenomena such as entanglement and superposition to encode data efficiently and process calculations in parallel, rather than in sequence like classical computers. As such, the processing power increases exponentially the more qubits you add to a system for certain types of problems. But these quantum states are inherently fragile, and even the tiniest interaction with their environment can cause them to collapse.

That’s why quantum computers go to great lengths to separate their qubits from external disturbances. This is normally done by keeping them at ultra-low temperatures or in a vacuum — or by encoding them into photons that interact weakly with the environment.

Related: Quantum computers will be a dream come true for hackers, risking everything from military secrets to bank information. Can we stop them?

But even then, errors can creep in, and occur at much greater rates than in classical devices. Logical operations in Google’s state-of-the-art quantum processor fail at a rate of about 1 in 100, says Roffe.

"We have to find some way of bridging this gulf so that we can actually use quantum computers to run some of the really exciting applications that we've proposed for them," he said.

QEC schemes build on top of ideas developed in the 1940s for early computers, which were much more unreliable than today’s devices. Modern chips no longer need error correction, but these schemes are still widely used in digital communications systems that are more susceptible to noise.

They work by building redundancy into the information being transmitted. The simplest way to implement this is to simply send the same message multiple times, Roffe said, something known as a repetition code. Even if some copies feature errors, the receiver can work out what the message was by looking at the information that is most often repeated.

But this approach doesn’t translate neatly to the quantum world, says Roffe. The quantum states used to encode information in a quantum computer collapse if there is any interaction with the external environment, including when an attempt is made to measure them. This means that it's impossible to create a copy of a quantum state, something known as the "no-cloning theorem." As a result, researchers have had to come up with more elaborate schemes to build in redundancy.

What is a logical qubit and why is it so important?

The fundamental unit of information in a quantum computer is a qubit, which can be encoded into a variety of physical systems, including superconducting circuits, trapped ions, neutral atoms and photons (particles of light). These so-called "physical qubits" are inherently error-prone, but it’s possible to spread a unit of quantum information across several of them using the quantum phenomenon of entanglement.

This refers to a situation where the quantum states of two or more particles are intrinsically linked with each other. By entangling multiple physical qubits, it's possible to encode a single shared quantum state across all of them, says Roffe, something known as a "logical qubit." Spreading out the quantum information in this way creates redundancy, so that even if a few physical qubits experience errors, the overarching information is not lost.

However, the process of detecting and correcting any errors is complicated by the fact that you can’t directly measure the states of the physical qubits without causing them to collapse. "So you have to be a lot more clever about what you actually measure," Dominic Williamson, a research staff member at IBM, told Live Science. "You can think of it as measuring the relationship between [the qubits] instead of measuring them individually."

This is done using a combination of "data qubits" that encode the quantum information, and "ancilla qubits" that are responsible for detecting errors in these qubits, says Williamson. Each ancilla qubit interacts with a group of data qubits to check if the sum of their values is odd or even without directly measuring their individual states.

If an error has occurred and the value of one of the data qubits has changed, the result of this test will flip, indicating that an error has occurred in that group. Classical algorithms are used to analyze measurements from multiple ancilla qubits to pinpoint the location of the fault. Once this is known, an operation can be performed on the logical qubit to fix the error.

What are the main error-correction approaches?

While all QEC schemes share this process, the specifics can vary considerably. The most widely-studied family of techniques are known as "surface codes," which spread a logical qubit over a 2D grid of data qubits interspersed with ancilla qubits. Surface codes are well-suited to the superconducting circuit-based quantum computers being developed by Google and IBM, whose physical qubits are arranged in exactly this kind of grid.

But each ancilla qubit can only interact with the data qubits directly neighboring it, which is easy to engineer but relatively inefficient, Williamson said. It’s predicted that using this approach, each logical qubit will require roughly 1,000 physical ones, he adds.

This has led to growing interest in a family of QEC schemes known as low-density parity check (LDPC) codes, Williamson said. These rely on longer-range interactions between qubits, which could significantly reduce the total number required. The only problem is that physically connecting qubits over larger distances is challenging, although it is simpler for technologies like neutral atoms and trapped ions, in which the physical qubits can be physically moved around.

A prerequisite for getting any of these schemes working, though, says Roffe, is slashing the error rate of the individual qubits below a crucial threshold. If the underlying hardware is too unreliable, errors will accumulate faster than the error correction scheme can resolve them, no matter how many qubits you add to the system. In contrast, if the error rate is low enough, adding more qubits to the system can lead to an exponential improvement in error suppression.

The recent Google paper has provided the first convincing evidence that this is within reach. In a series of experiments, the researchers used their 105-qubit Willow chip to run a surface code on increasingly large arrays of qubits. They showed that each time they scaled up the number of qubits, the error rate halved.

"We want to be able to be able to suppress the error rate by a factor of a trillion or something so there's still a long way to go," Roffe told Live Science. "But hopefully this paves the way for larger surface codes that actually meaningfully suppress the error rates to the point where we can do something useful."

Scientists have discovered how to use a quantum material to tap into the power of magnetism to store quantum information — thanks to its capacity to support magnetic switching (when the magnetic polarization switches direction). They say it can lead to more viable quantum computing and sensing, thanks to much longer-lasting quantum states.

Chromium sulfide bromide is an unusual material that has been likened to filo pastry (thin, folded layers of pastry) thanks to its structure of just a few layers of atoms. Scientists consider it extremely promising for quantum devices because many of its properties can be used for any type of information storage. It can be used to store information using an electric charge, as photons (as light), through magnetism (through the electronic spin) and even via phonons — like vibrations from sound.One of the many ways in which chromium sulfide bromide could be used to store information is through excitons — quasi-particles that form when an electron and its hole become bound together. When a photon is moved from its grounded energy state, it effectively leaves behind a hole where it once was. Although they are separated, the photon and the hole remain paired together and become known as an exciton.

Previous research has highlighted how these excitons can sometimes form in a straight line in the material. But these excitons also exhibit unusual magnetic properties.

At temperatures less than 132 Kelvin (-222 degrees F or -141 degrees C), the material's layers are magnetized and the electrons are aligned,while the direction of the magnetic field switches for each layer in the material.

When chromium sulfide bromide is warmed to more than 132 K, the material loses its magnetization as the electrons can move in random directions. In this unmagnetized state, the excitons are no longer trapped and extend over multiple layers of the material.

However, when chromium sulfide bromide is only a single atom thick, the excitons are confined to a single dimension. When used in a quantum device, this restriction could allow quantum information in the excitons to be stored much longer than it would otherwise be, as the excitons are less likely to collide with each other and lose the information they carry through decoherence (the loss of quantum information due to interference).

Quantum information in one dimension

In the new study published Feb. 19 in the journal Nature Materials, scientists reported that they had produced excitons in chromium sulfide bromide by firing pulses of infrared light in 20 bursts lasting only 20 quadrillionths of a second (20 x 10^-15). They then used a second infrared laser to nudge the excitons into a higher energy state, before finding they had created two different variations of exciton when they should otherwise have had identical states of energy.

When the less energetic pulses were shot by lasers from different axes, the researchers discovered that the direction-dependent excitons could be confined to a single line or expanded into three dimensions. The change from unidimensional; to three-dimensional excitons accounted for how long the excitons could last without colliding with each other.

"The magnetic order is a new tuning knob for shaping excitons and their interactions. This could be a game changer for future electronics and information technology," said co-author of the study Rupert Huber, professor of experimental and applied physics at the University of Regensburg, Germany.

One of the key areas the research team wants to pursue next is to investigate whether these excitons could be converted to magnetic excitations in the electronic spin of the material. Were they to achieve this, it could provide a useful method for converting quantum information between different subatomic particles (photons, excitons and electrons).

Switching between magnetized and non-magnetized states could provide a fast method for converting photon and spin-based quantum information. The hope with chromium sulfide bromide is to harness all of its properties for use in future devices.

"The long-term vision is, you could potentially build quantum machines or devices that use these three or even all four of these properties: photons to transfer information, electrons to process information through their interactions, magnetism to store information, and phonons to modulate and transduce information to new frequencies," said co-author of the study Mackillo Kra, professor of electrical and computer engineering at the University of Michigan, in a statement.

Quantum computing company Alice & Bob has improved the reliability of its cat qubits, which could make tomorrow's quantum computers far more accurate.

Fault tolerance is a major challenge in quantum computing. This is because the qubits in quantum computers are "noisy" and susceptible to decoherence — the loss of quantum information due to interference from the external environment. Improving qubits' reliability by implementing fault-tolerant technologies has therefore been a key research area.

There has been a particular drive to suppress the error rates associated with bit-flipping (when a qubit switches the probabilities of measuring 0 or 1). But previously, this had been found to lead to increased errors with phase-flipping (when a qubit switches its probabilities of being positive or negative).

Cat qubits are a type of qubit that mimics the superposition principle of Schrödinger’s cat — a thought experiment which postulates that a cat in a box with a randomly activated poison might be considered as both alive and dead until it is directly observed.

"Cat qubits" are designed to reduce bit-flips, thereby reducing the resources required for error correction. Cat qubits have been studied by multiple research teams, with qubits created by Alice & Bob scientists even incorporated into the Ocelot Chip, manufactured by Amazon Web Services (AWS).

Previous Alice & Bob research has demonstrated that cat qubits could achieve a bit-flip lifetime of 138 milliseconds.

But in a new study uploaded Feb. 28 to the pre-print arXiv database, scientists outlined a new way to stabilize cat qubits, with better bit-flip protection of up to 160 times, equating to a cat qubit lifetime of 22 seconds. The effect on the phase-flip rate was minimal.

The team achieved this by compressing the quantum states of cat qubits such that there is a smaller overlap between the two states. For these squeezed cat qubits, they demonstrated a steep reduction in bit-flip error rate as photon numbers increased.

The technique demonstrated in this research is especially useful, as it does not require any modifications to the design of the circuit. "Squeezing" cat qubits will therefore make error correction less resource-intensive than previous methods.

The next stage in Alice & Bob’s research will aim to develop universal fault-tolerant quantum computing, where bit-flips and phase-flips can be efficiently managed. This could lead to practical applications in fields such as chemistry and materials science.

Researchers have observed an elusive quantum phenomenon that was first predicted more than 50 years ago. This process, which forms a new state of matter, may have ramifications for future quantum computing.

The phase, called a superradiant phase transition (SRPT), is the result of two independent groups of quantum particles beginning to fluctuate in a way that's both coordinated and collective, the scientists said in a new study published April 4 in the journal Science Advances.

In this case, the two groups of particles were iron ions and erbium ions inside a crystal. Researchers were able to induce the phenomenon by applying a magnetic field — over 100,000 times stronger than the Earth’s — to a crystal made of erbium, iron and oxygen after cooling it to -457 °F (-271.67 °C), temperatures nearing absolute zero.

Under those conditions, the team was able to observe unmistakable signatures of an SRPT within the crystal. Their observations exactly matched predictions of what an SRPT would look like according to a famous model formulated by Robert H. Dicke in 1954.

The so-called Dicke model was the first to describe the phenomenon of superradiance — where excited atoms emit light faster than normal atoms — and laid the groundwork for understanding the superradiant phase transition as a distinct state of matter arising from strong interactions between light and matter. It was further elaborated on by Klaus Hepp and Elliot H. Lieb in 1973 who formally demonstrated the existence of this phase transition.

Related: Government scientists discover new state of matter that's 'half ice, half fire'

"Originally, the SRPT was proposed as arising from interactions between quantum vacuum fluctuations — quantum light fields naturally existing even in completely empty space — and matter fluctuations," said co-lead author Dasom Kim, a doctoral student in applied physics at Rice University, in a statement. "However, in our work, we realized this transition by coupling two distinct magnetic subsystems — the spin fluctuations of iron ions and of erbium ions within the crystal."

Spin describes the angular momentum of an elementary particle or atom. It dictates the behavior in magnetic fields and is important for determining the statistical properties of collections of particles, which, in turn, influence the structure of matter and the nature of fundamental forces. When excitation created by thermal fluctuations, alternating magnetic fields or other sources causes a wave-like disturbance across a pattern of spins in a material, it's called a magnon.

In the past, SRPT was branded a "no-go theorem" because it violated a fundamental limitation of light-based systems. But creating a magnonic version of the phenomenon allowed the team to bypass this restriction. In their experiment, the iron ions’ magnons play the role normally occupied by vacuum fluctuations, and the erbium ions’ spins fill in for matter fluctuations.

Researchers were able to clearly observe the disappearance of one spin mode's energy signal and a shift in the other — unmistakable evidence of an SRPT.

"We established an ultrastrong coupling between these two spin systems and successfully observed a SRPT, overcoming previous experimental constraints," Kim said.

The unique characteristics of an SRPT could have important implications for a diverse number of quantum technologies. This is due to a phenomenon called quantum squeezing, where fluctuations are reduced in one measurable property of a quantum system below the standard quantum limit (though fluctuations increase in another property).

"Near the quantum critical point of this transition, the system naturally stabilizes quantum-squeezed states — where quantum noise is drastically reduced — greatly enhancing measurement precision," Kim said in the statement. "Overall, this insight could revolutionize quantum sensors and computing technologies, significantly advancing their fidelity, sensitivity and performance."

There are further advantages beyond the precision of quantum measurements and computations due to an SRPT stabilizing quantum squeezed states, as well. Because SRPT arises from the collective behavior of many quantum particles, it could provide a form of built-in protection against individual qubit errors and decoherence, which are major hurdles in current quantum computing. The synchronized behavior could lead to more robust and stable qubits with longer coherence times. It's also possible that the strong, coherent interactions within an SRPT could lead to faster gates (the building blocks of quantum algorithms).

Quantum physics is the branch of science that deals with the tiniest particles in the universe, such as atoms, electrons, photons (light particles), and other subatomic particles like quarks.

In the everyday world, at the scale that we can see, things tend to follow the laws of classical physics. However, when you zoom all the way in to the smallest particles, classical physics stops working quite as well, and the rules of quantum mechanics come into play.

Some of the key concepts of quantum physics are that particles like electrons can behave as waves, and vice versa (known as wave-particle duality); two particles can be linked in such a way that if you measure one, you instantly know something about the other (quantum entanglement); and a quantum particle can be in multiple states at once until it's observed (quantum superposition).

What is quantum superposition?

In everyday life, something can only be in one state at a time: a light switch is either on or off, a cat is either dead or alive. In the quantum world, things don't work quite the same way. Quantum superposition describes how a quantum particle, like an electron, a photon, or even an atom, can exist in multiple different states at the same time — until it's measured. Before it's observed, it's not halfway between states, but is instead a "superposition" of the two at once.

In quantum physics, the state of a particle is described by a wave equation, which tells us the probabilities of where a particle might be or what its properties might be. This probability wave can exist in a blend of multiple states.

What is Schrödinger’s Cat?

Schrödinger’s Cat is a famous thought experiment that illustrates how superposition works. Imagine a cat in a box with a mechanism that has a 50/50 chance of killing it, depending on whether or not a quantum particle decays radioactively, spontaneously changing into a different type of atom and releasing radioactive particles like electrons.

Until someone opens the box and observes it, the cat is considered to be in a superposition of both alive and dead. When you measure or observe the system — or in the case of Schrödinger’s Cat look inside the box — the superposition settles into one definite state, and the cat's fate is discovered.

Related: Physicists create hottest Schrödinger's cat ever in quantum technology breakthrough

Quantum superposition has been experimentally observed by scientists on multiple occasions. One famous example is the double-slit experiment, where photons are fired at a barrier with two slits, behind which is a a screen that records where the particles land. If you send particles through one slit, you get a single band on the screen, but if you open both, you get a wave-like interference pattern with multiple bands on the screen, which also proves that particles and waves can act like each other. Sending one particle at a time, you would expect each one to go through one slit or the other. However, the interference pattern still builds up, as if each single particle is interfering with itself. This means that each single particle is somehow going through both slits at once, and therefore is in a superposition of both possibilities

If you try to measure which slit the particle goes through, the superposition collapses: the particle does appear to have passed through a single slit, and the interference pattern disappears, leaving only two bands on the screen.

Additionally, ions and larger molecules have been experimentally trapped in a superposed state, and chlorophyll in the leaves of plants has been discovered to use quantum superposition to more efficiently harvest light from the sun.

Why is superposition so important in quantum computing?

Quantum superposition is also used as a tool in quantum computing and is the main reason quantum computers can be so powerful.

A classical binary bit can only be in one state at a time: 0 or 1. These bits are encoded on transistors, usually made from silicon, germanium or other semiconductors. With three bits present, they can have a potential of 8 different states: 000, 001, 010, 011, 100, 101, 110, and 111. To process all possibilities, a classical computer has to check them one at a time.

In quantum computers, particles such as electrons or photons act as a qubit (quantum bit), which can be in a superposition of both 0 and 1. Three qubits can be in a superposition of all 8 possible states at once, meaning that quantum computers can process a much larger number of calculations simultaneously. With three qubits present, a quantum computer could process all eight states listed above at once.

This much greater processing power than traditional computers could mean that quantum computers could one day be used to perform complex simulations in pharmaceuticals, climate modeling, and manufacturing. In theory, a quantum computer powerful enough can perform calculations in seconds that would have taken the most powerful supercomputers millions of years to complete.

When is World Quantum Day?

World Quantum Day, an international celebration held to promote public understanding of quantum science, is held annually on April 14.

The date, 4/14, was chosen because 4.14 represents the first three digits of Planck’s constant (4.135667696 x 10^-15 electron volts per hertz, rounded to 4.14 x 10^-15) — an important number in quantum physics.

Physicists have created a Schrödinger's cat state at unusually hot temperatures, and it could be a major step toward the development of practical quantum computers.

Schrödinger's cat states exist in two distinct quantum states simultaneously and take their name from Erwin Schrödinger's famous thought experiment of a cat that is both simultaneously alive and dead.

Yet to achieve these states, quantum objects usually have to be cooled to their ground states, which exist just a few fractions above absolute zero (minus 459.67 degrees Fahrenheit or minus 273.15 degrees Celsius).

But now, a team of scientists has shown that a state of quantum superposition can be achieved at significantly warmer temperatures than before. The researchers published their findings April 4 in the journal Science Advances.

"Schrödinger also assumed a living — i.e., 'hot' — cat in his thought experiment," study co-author Gerhard Kirchmair, a physicist at the University of Innsbruck in Austria, said in a statement. "We wanted to know whether these quantum effects can also be generated if we don't start from the 'cold' ground state."

In Schrödinger's thought experiment, the weird rules of the quantum world are envisioned by imagining a cat placed inside an opaque box with a poison vial whose release mechanism is controlled by radioactive decay — a completely random quantum process. Until the box is opened and the cat is observed, Schrödinger said, the rules of quantum mechanics mean that the unfortunate feline should exist in a superposition of states, simultaneously dead and alive.

Related: World's 1st modular quantum computer that can operate at room temperature goes online

As most quantum effects typically decohere and disappear at larger scales, Schrödinger's analogy was meant to demonstrate the fundamental differences between our world and the world of the very small.

Usually, quantum states of this kind can only be achieved at extremely low temperatures. This means that the qubits (quantum bits) found inside quantum computers have to be maintained inside extremely cold cryostats in order for them to not decohere and lose their information.

Yet no hard limit between the quantum realm and ours exists, and physicists have had past success cajoling larger objects into showing weird quantum behavior.

With this in mind, the physicists behind the new research placed a qubit inside a microwave resonator. After some careful tweaking, they nudged the qubit into a state of superposition at a temperature of 1.8 kelvins (minus 456.43 F or minus 271.35 C). This is still a very chilly temperature, but it is 60 times hotter than the ambient temperature in the cavity.

"Many of our colleagues were surprised when we first told them about our results, because we usually think of temperature as something that destroys quantum effects," study co-author Thomas Agrenius, a doctoral student at the Institute of Photonic Sciences in Barcelona, said in the statement. "Our measurements confirm that quantum interference can persist even at high temperatures."

While likely too incremental to have an immediate practical impact, the scientists' findings could one day liberate quantum computing from the necessity of storing the computers in extremely cold environments — especially if researchers can continue to raise the temperatures at which superposition can be achieved.

"Our work reveals that it is possible to observe and use quantum phenomena even in less ideal, warmer environments," Kirchmair said. "If we can create the necessary interactions in a system, the temperature ultimately doesn't matter."

Scientists have discovered a groundbreaking method to shield quantum information from "noise" — and it could finally let us build practical quantum computers.

Quantum computers rely on quantum entanglement, the connection between the quantum properties of two particles that are shared instantaneously across time and space. This enables quantum computers to perform faster calculations than their traditional counterparts because they can process information in parallel rather than in sequence.

But maintaining this "coherence" is difficult due to "noise" from the outside world, as interactions with loose particles, rays of light and even minute changes in temperature can break the entanglement and disperse the information within. That's why the error rate in qubits is much higher than in conventional bits in classical computing.

"Basically even though companies claim [they have] 1,000 qubits, very few of them are useful. Noise is the reason," study co-author Andrew Forbes, a professor of physics at the University of Witwatersrand in Johannesburg, South Africa told Live Science. "Everyone agrees that there is no point in pushing for more qubits unless we can make them less noisy."

Now, by encoding the information in the topology (or the properties that stem from the shape) of two entangled photons, a team of physicists has found a way to preserve quantum information, even amid a storm of noise. The researchers published their findings on March 26 in the journal Nature Communications.

Related: MIT invents new way for QPUs to communicate — paving the way for a scalable 'quantum supercomputer'

In much the same way that traditional computer bits are the basic units of digital information, qubits encode quantum information. Like bits, qubits can exist as a 1 or a 0, representing the two possible positions in a two-state system.

Thanks to the bizarre rules of the quantum world, qubits can also exist in theoretically infinite superpositions of the two classical states. And when they're entangled inside quantum computers, their ability to crunch numbers grows exponentially.

But this quantum daisy chain is fragile: Even when housed inside extremely cold and highly insulated cryostats, current quantum computers are still infiltrated by tiny disturbances that rapidly disrupt the delicate processes within.

Quantum noise-cancelation

The typical strategy for preventing quantum decoherence is to preserve entanglement, but this has so far only enjoyed relative success. To look for a way around this, the researchers behind the new study sought to preserve information even in systems that had already been partially decohered.

"We decided to let the entanglement decay — it is always fragile so let it be so — and instead preserve information even with very little entanglement," Forbes said.

For their solution, Forbes and his colleagues turned to a type of qubit known as a "topological qubit" that encodes information in the shape made by two entangled particles. They settled on a quasiparticle known as an optical skyrmion, a wave-like field formed between two entangled photons.

After exposing the skyrmions to varying levels of noise, the researchers found that the patterns and information coded within remained resilient far beyond the point where non-topological systems would decohere.

"It turns out that so long as some entanglement remains, no matter how little, the topology stays intact," Forbes said. "The topology only disappears when the entanglement vanishes."

The scientists believe their approach could play a key role in making quantum computers and networks that can overcome noise in any environment. Their next step will be to create a "topological toolkit" that can encode practical information into a skyrmion and get it out again.

"Once we have this, we can start to think about using topology in practical situations, like communication networks and in computing," Forbes said.

Researchers have created a device that allows quantum processors to communicate with each other directly — an important step in developing practical quantum computers. It could mean both faster and less error-prone communication between processors.

Existing quantum architecture offers only limited communication between separate quantum processing units (QPUs). Such communication is "point-to-point," meaning that information has to be transferred in a chain across several nodes before reaching its destination. This increases the possibility of exposing the quantum information to noise and makes it more likely for errors to occur.

However, the new device developed by MIT scientists allows for "all-to-all" communication, so that all processors in a single network can communicate directly with any other processor. The researchers outlined their "remote entanglement" approach in a new study published March 21 in the journal Nature Physics.

Remote entanglement is a state where two particles share the same state, and changes to one automatically affect the other. The distance between the two can be vast, with no currently known limit.

In testing, the researchers connected two quantum processors by way of modules, each comprising four qubits. Some of the qubits in each module were tasked with sending photons, light particles that can be used to transmit quantum data, while others were assigned to storing data.

The modules were linked together with a superconducting wire called a waveguide, with the modules serving as an interface between the larger quantum processors and the waveguide. The scientists said that any number of processors could be connected in this way, creating a highly scalable network.

The researchers then used microwave pulses to spark an individual qubit into emitting photons in either direction across the waveguide.

Related: China achieves quantum supremacy claim with new chip 1 quadrillion times faster than the most powerful supercomputers

"Pitching and catching photons enables us to create a ‘quantum interconnect’ between nonlocal quantum processors, and with quantum interconnects comes remote entanglement,” said senior author of the study William D. Oliver, Associate Director of the Research Laboratory of Electronics at MIT, in a statement.

Photonic distortion

Entanglement is a state where two particles become connected and share information, even at vast distances. A change in one entangled particle will immediately affect its partner. It’s a critical phenomenon for quantum computing because it allows qubits to be correlated and act as a single system. This, in turn, lets us create algorithms that are impossible with classical computers.

However, just moving photons back and forth between modules doesn’t automatically create entanglement. To achieve that, the team had to specially prepare both the qubits and the photon, so that after being transferred, the modules shared a single photon.

To force the two modules to share the same photon, they had to interrupt photon emission pulses at the halfway point. This essentially meant that half of the photon was absorbed on the receiving end while half was retained by the emitting module.

The problem with this method is that the photon becomes distorted while traveling across the waveguide, which can impact absorption and interrupt entanglement. To overcome this flaw in the architecture, the team had to distort the photons to encourage maximum absorption. By distorting photons prior to transmission, they were able to raise absorption levels to 60%, enough to ensure entanglement.

The work is broadly applicable to practical quantum computing applications, according to lead author of the study Aziza Almanakly, an electrical engineering and computer science graduate student.

"In principle, our remote entanglement generation protocol can also be expanded to other kinds of quantum computers and bigger quantum internet systems," Almanakly said.

Physicists have discovered a new phase of matter, dubbed "half ice, half fire," that could open the door to new advancements in fields such as quantum computing.

The new phase combines a number of "up" spins of electrons within an atom, which are highly ordered and referred to as cold cycles, with a number of "down" spins, which are highly disordered and referred to as hot cycles — lending the phase its nickname, "half ice, half fire."

"Half ice, half fire" is a significant discovery not only because of its novelty but also because it can produce sharp switching between phases at reasonable temperatures. It's the twin of the "half fire, half ice" state first observed by the same team at Brookhaven National Laboratory — physicists Weiguo Yin and Alexei Tsvelik, alongside their then intern, Christopher Roth — back in 2016.

These discoveries provide insight into some of the central questions in physics and the materials sciences, according to the team, as well as advance the ability to identify new states of matter with exotic properties and manipulate the transition between those states.

"Solving those problems could lead to great advances in technologies like quantum computing and spintronics," Yin said in a statement from Brookhaven National Lab. Tsvelik added that the team's findings "may open a new door to understanding and controlling phases and phase transitions in certain materials."

Related: Exotic new state of matter discovered by squishing subatomic particles into an ultradense crystal

"Missing pieces of the puzzle"

Yin and Tsvelik first discovered "half ice, half fire" when performing research on a type of magnetic material called a ferrimagnet. Ferrimagnets have populations of atoms with opposing magnetic moments, but because the populations are unequal, some magnetization remains.

The specific ferrimagnet in which "half ice, half fire" was observed is Sr3CuIrO6, a compound that consists of strontium, copper, iridium and oxygen. It's the same material in which the team originally discovered "half fire, half ice," which they induced, or caused to occur within the ferrimagnet, by exposing the material to an external magnetic field. In "half fire, half ice," hot spins occurred on the copper sites and had smaller magnetic movements, while the iridium sites yielded cold spins with larger magnetic movements.

Although it was an exciting discovery, Tsvelik admitted that it was only a first step.

"Despite our extensive research, we still didn't know how this state could be utilized," he said. "We were missing pieces of the puzzle."

Now, the recent work, spearheaded by Yin, has revealed that "half fire, half ice" has a hidden and opposite state in which the hot and cold spins swap positions. The team identified an extremely narrow temperature range in which the switch between phases takes place, which has promising implications for a number of fields.

Commercially, this kind of ultrasharp phase switching could lead to advances in refrigeration technology. It may even be possible to utilize the phases themselves as bits in a novel approach to quantum information storage. "The door to new possibilities is now wide open," Yin said.

The team's research into the new phase was published in the journal Physical Review Letters in December 2024.

Researchers in China have developed a quantum processing unit (QPU) that is 1 quadrillion (10¹⁵) times faster than the best supercomputers on the planet.

The new prototype 105-qubit chip, dubbed "Zuchongzhi 3.0," which uses superconducting qubits, represents a significant step forward for quantum computing, scientists at the University of Science and Technology of China (USTC) in Hefei said.

It rivals the benchmarking results set by Google's latest Willow QPU in December 2024 that allowed scientists to stake a claim for quantum supremacy — where quantum computers are more capable than the fastest supercomputers — in lab-based benchmarking.

The scientists used the processor to complete a task on the widely used quantum computing random circuit sampling (RSC) benchmark in just a few hundred seconds, they said in a new study published March 3 in the journal Physical Review Letters.

This test, 83-qubit, 32-layer random circuit sampling task, was also completed 1 million times faster than the result set by Google's previous generation Sycamore chip, published in October 2024. Frontier, the second-fastest supercomputer in the world, would only be able to complete the same task in 5.9 billion years, by contrast

Related: World's 1st modular quantum computer that can operate at room temperature goes online

Although the results suggest QPUs are capable of achieving quantum supremacy, the specific RCS benchmarking used favors quantum methods. Also, improvements in classical algorithms that drive classical computing may close the gap, as happened in 2019 when Google scientists first announced a quantum computer had outperformed a classical computer — in the first use of the RSC benchmark.

"Our work not only advances the frontiers of quantum computing, but also lays the groundwork for a new era where quantum processors play an essential role in tackling sophisticated real-world challenges," the scientists said in the study.

Rivaling Google's best quantum processor

The latest iteration of Zuchongzhi includes 105 transmon qubits — devices made from metals like tantalum, niobium, and aluminum that have reduced sensitivity to noise — in a 15-by-7 rectangular lattice. This builds on the previous chip, which included 66 qubits.

One of the most important areas critical to the viability of quantum computing in real-world settings is coherence time, a measure of how long a qubit can maintain its superposition and tap into the laws of quantum mechanics to perform calculations in parallel. Longer coherence times mean more complicated operations and calculations are possible.

Another major improvement was in gate fidelity and quantum error correction, which has been an obstacle to building useful quantum computers. Gate fidelity measures how accurately a quantum gate performs its intended operation, where a quantum gate is analogous to a classical logic gate, performing a specific operation on one or more qubits, manipulating their quantum state. Higher fidelity qubits mean fewer errors and more accurate computations.

Zuchongzhi 3.0 performed with an impressive parallel single-qubit gate fidelity of 99.90%, and a parallel two-qubit gate fidelity of 99.62%. Google's Willow QPU edged it slightly, with results of 99.97% and 99.86% respectively.

These improvements were largely possible due to engineering improvements, including enhancements in fabrication methods and better optimized qubits design, the scientists said in the study. For instance, the latest iteration lithographically defines qubit components using tantalum and aluminum, bonded through an indium bump flip-chip process. This improves accuracy and minimizes contamination.

Scientists have developed a quantum computer that uses light to process data, paving the way for quantum computers that can operate in a networked environment at room temperature.

The new system, called Aurora, is the first photonic quantum computer in the world that can operate at scale using several modules interconnected through fiber optic cables. The system presents a solution to some of quantum computing's biggest problems — namely operation at scale, fault tolerance and error correction, Xanadu representatives say.

This breakthrough could lead to the creation of viable quantum data centers with higher fault tolerance and lower error rates than we can otherwise achieve today, the researchers said in a study published Jan. 22 in the journal Nature.

"The two big challenges remaining for the industry are the improved performance of the quantum computer (error correction and fault tolerance) and scalability (networking)," Christian Weedbrook, the founder and CEO of Xanadu, the company behind the new system, said in a statement.

Traditional qubits, or superconducting qubits, are the building blocks of quantum computing and hold the key to processing massive amounts of data quickly.

Related: Quantum internet breakthrough after 'quantum data' transmitted through standard fiber optic cable for 1st time

But these qubits use microwave signals to help process data, which creates heat that can damage hardware. Further, current cooling methods, which are used to create a near absolute zero computing environment, also damage hardware and make accessing machines difficult.

By using light-based, or photonic, qubits instead of microwave or superconducting qubits, Weedbrook and his team created a light-based system that uses networked photonic chips. This makes Aurora inherently connectible, as fiber optics make up the basis of the global networking system.

Light-powered quantum computing networks

Aurora's developers posit that by breaking quantum computers into smaller, less error-prone components, they can strengthen quantum error correction by interconnecting the units.

"The fundamental problem of fault tolerance and finding ways to error-correct the quantum states faster than the errors occur remains a big challenge to performing any useful computations," said Darran Milne, doctor of quantum information theory and CEO of tech company VividQ, who was not involved in the project.

"Rather than trying to compute with a single large quantum computer it seems they [Xanadu] are trying to split it into smaller simpler systems that might be easier to error-correct individually," Milne told Live Science. "It remains to be seen if that actually makes the problem any better or just multiplies the errors."

The framework relies on technology used in the company's X8 (quantum computing hardware) and Borealis (single-system quantum computer). The system utilizes 35 photonic chips connected through 8 miles (13 kilometers) of fiber optic cables.

"Photonics really is the best and most natural way to both compute and network," the researchers said in the statement. "We now could, in principle, scale up to thousands of server racks and millions of qubits."

Potential applications of the Aurora photonic quantum computer framework include simulating molecules and calculating potential outcomes of pharmaceutical trials, potentially eliminating the need for long drug trials. Photonic quantum computers might also usher in the age of highly secure, encrypted communications known as quantum cryptography.

The team at Xanadu next plan to focus on eliminating weakened fiber optic signals due to optical loss.

Scientists have used AI to discover an easier method to form quantum entanglement between subatomic particles, paving the way for simpler quantum technologies.

When particles such as photons become entangled, they can share quantum properties — including information — regardless of the distance between them. This phenomenon is important in quantum physics and is one of the features that makes quantum computers so powerful.

But the bonds of quantum entanglement have typically proven challenging for scientists to form. This is because it requires the preparation of two separate entangled pairs, then measuring the strength of entanglement — called a Bell-state measurement — on a photon from each of the pairs.

These measurements cause the quantum system to collapse and leave the two unmeasured photons entangled, despite them never having directly interacted with one another. This process of “entanglement swapping” could be used for quantum teleportation.

In a new study, published Dec. 2, 2024 in the journal Physical Review Letters, scientists used PyTheus, an AI tool that has been specifically created for designing quantum-optic experiments. The authors of the paper initially set out to reproduce established protocols for entanglement swapping in quantum communications. However, the AI tool kept producing a much simpler method to achieve quantum entanglement of photons.

Related: Quantum data beamed alongside 'classical data' in the same fiber-optic connection for the 1st time

"The authors were able to train a neural network on a set of complex data that describes how you set up this kind of experiment in many different conditions, and the network actually learned the physics behind it," Sofia Vallecorsa, a research physicist for the quantum technology initiative at CERN, who was not involved in the new research, told Live Science.

Tapping into AI to simplify quantum entanglement

The AI tool proposed that entanglement could emerge because the path of photons were indistinguishable: when there are several possible sources the photons could have come from, and if their origins become indistinguishable from one another, then entanglement can be produced between them when none existed before.

Although the scientists were initially skeptical of the results, the tool kept returning the same solution so they tested the theory. By adjusting the photon sources and ensuring they were indistinguishable, the physicists created conditions where detecting photons at certain paths guaranteed that two others emerged entangled.

This breakthrough in quantum physics has simplified the process by which quantum entanglement can be formed. In future, it could have implications for the quantum networks used for secure messaging, making these technologies much more feasible.

"The more we can rely on simple technology, the more we can increase the range of applications," Vallecorsa said. "The possibility to build more complex networks, that could branch out in different geometries, could have a big impact with respect to the single end-to-end case."

Whether it is practical to scale the technology into a commercially viable process remains to be seen, however, as environmental noise and device imperfections could cause instability in the quantum system.

The new study has also provided a convincing argument for the use of AI as a research tool by physicists. "We are looking more into introducing AI, but there is still a little bit of scepticism, mostly due to what the role of the physicist is going to be once we start going that way," Vallecorsa said. "It is an opportunity for getting a very interesting result and shows in a very compelling way how this can be a tool that physicists use."

Amazon Web Services (AWS) has launched a prototype quantum computing chip that is the first in the world to be fitted with error-resistant "cat qubits" — basic units of quantum computing information inspired by the famous Schrödinger's cat thought experiment.

The quantum processing unit (QPU), named "Ocelot," includes five data qubits, or cat qubits, to store information; five buffer circuits made from the superconductor tantalum to stabilize the cat qubits; and four additional qubits to detect errors that occur during data processing.

These internal components are divided across two integrated silicon microchips that each measure roughly 0.16 square inches (1 square centimeter), making the device small enough to fit on the tip of your finger.

The new architecture is designed to significantly reduce the cost and energy needed to slash errors that occur naturally in quantum computers — a challenge scientists are still trying to find a solution to (with progress made in a February 2024 study and another in April last year, among others).

Significantly, the researchers said the new technology could exponentially reduce errors as more qubits are added to future versions of the chip. They outlined their findings in a new study published Feb. 26 in the journal Nature.

Turning down the quantum noise

Because qubits are inherently "noisy" — meaning they're sensitive to disturbances from vibrations, heat, electromagnetic interference and radiation from space — they are far more prone to failing than classic bits. The error rate in classic bits is 1 in 1 million million, versus roughly 1 in 1,000 in qubits. This far higher error rate often leads to the collapse of any quantum superposition mid-calculation and failures when quantum computations are being performed.

The two types of error are bit-flip errors, where the probability of measuring 0 becomes the probability of measuring 1; and phase-flip errors, where a qubit rotates 180 degrees on its vertical axis. Bit-flip errors affect both bits and qubits, while phase-flip errors affect only qubits. The need to correct both types of error in quantum systems requires significant resources compared with error correction in classical computing.

Related: Google 'Willow' quantum chip has solved a problem the best supercomputer would have taken a quadrillion times the age of the universe to crack

Because of this, scientists say that a quantum computer would need millions of qubits before getting close to achieving "quantum supremacy" — which would be unfeasible in terms of the physical space, energy and resources required to build and run such a hypothetical machine. This is why more research is focused on building reliable qubits integrated with error correction technologies.

"Logical qubits" — which are made up of multiple physical qubits that store the same information to spread the points of failure — are the prevailing error-correction method. AWS researchers, however, say that without further improvements to the hardware, current approaches come at a huge and prohibitive cost, because they would need thousands of physical qubits to form one logical qubit capable of achieving low error rates.

Ocelot, however, adopts the cat qubit design developed by the French startup Alice & Bob. Named after the famous Schrödinger's cat thought experiment, this qubit is designed in such a way that it is inherently resistant to bit-flip errors.

Tapping into new 'cat qubits'

Unlike the conventional superconducting qubits used in machines built by the likes of IBM and Google that can achieve a superposition of 1 and 0, cat qubits can achieve a double superposition of two quantum states simultaneously. Alice & Bob scientists outlined how this technology works in a roadmap and white paper published in 2024.

The cat qubit uses a quantum superposition of classical-like states of well-defined amplitude and phase to encode information. It uses bosonic particles specifically to encode data — in this case, photons, or particles of light.

The more energy is pumped into the system, the more photons are created, and the more amplitudes, or oscillator states, can be accessed, which better protects quantum information. Increasing the number of photons in the oscillator can make the rate of bit-flip errors exponentially smaller, the scientists said. This means that, to reduce the error rate, you don't need to increase the qubit count; rather, you need to increase the energy of the oscillator.

Previous experiments over the last decade have shown the potential of cat qubits in single-qubit demonstrations, including a study from a different team in 2015 and one as recently as May 2024. A study published in January this year also outlined an approach to error correction that was inspired by Schrödinger's cat. However, AWS's Ocelot is the first example of a coherent multi-cat qubit system integrated into a chip built using existing fabrication methods.

In the new study, the scientists demonstrated measurements taken with Ocelot that show bit-flip errors are exponentially suppressed at the physical qubit level, while phase-flip errors are corrected using the simplest error-correcting code, known as repetition code. The gates between the cat qubits and error-correcting qubits are also effective at detecting phase-flip errors, while preserving the power of the cat qubits to protect against bit-flip errors.

The results showed bit-flip times approaching 1 second — roughly 1,000 times longer than the lifetime of conventional superconducting qubits. This was accomplished using four photons, enabling phase-flip times measured in tens of microseconds, which is sufficient for quantum error correction.

The scientists then tested the system to determine how effective this architecture could be at behaving like a logical qubit. The total logical error rate was 1.72% when running code on three cat qubits, versus 1.65% when using five cat qubits. With nine qubits in total (five cat and four error-correcting), they hit error rates comparable to a system with 49 physical qubits.

Scalable quantum computing

The scientists estimate that using the architecture in Ocelot, a future quantum computer with "transformative societal impact" needs as little as one-tenth of the resources that would otherwise be needed with standard approaches to quantum error-correction.

"Future versions of Ocelot are being developed that will exponentially drive down logical error rates, enabled by both an improvement in component performance and an increase in code distance," co-authors of the study, Fernando Brandão, a Caltech professor of theoretical physics, and Oskar Painter, professor of applied physics at Caltech, said in a technical blog post. "Codes tailored to biased noise, such as the repetition code used in Ocelot, can significantly reduce the number of physical qubits required," they said.

"We believe that Ocelot's architecture, with its hardware-efficient approach to error correction, positions us well to tackle the next phase of quantum computing: learning how to scale," Brandão and Painter added. "Scaling using a hardware-efficient approach will allow us to achieve more quickly and cost-effectively an error-corrected quantum computer that benefits society."

Scientists at Google have revealed a new method of "quantum simulation" that uses computing power to mimic the behavior of a powerful quantum system. This approach, they argue, could lead to quantum computers that can overtake supercomputers within five years and lead to breakthroughs in drug discovery and battery development.

Quantum simulation is a process in which computers simulate physical processes and large quantum systems, such as complex molecules. Essentially, engineers simulate physical processes that are dominated by the effects of quantum physics.

But this is difficult to do with classical computers because you have to model every particle's interaction with every other particle. Because subatomic particles have a probability of being in multiple states at once and can be entangled with each other, the complexity of these calculations skyrockets quickly as you scale the number of particles involved.

Instead, scientists are turning to quantum computers, whose behavior is already governed by the laws of quantum mechanics, to solve the problems. Because quantum physics is built into the way these systems work. If the qubits are entangled or linked together the right way, they can mimic bigger quantum systems without having to explicitly calculate every step in the evolution of the system.

That is where "quantum simulation" comes into play. There are two types of quantum simulation. Digital simulation lets researchers selectively pivot between quantum states by entangling and disentangling different qubit pairings (two entangled qubits) in series. Analog simulation, meanwhile, is much faster. This involves entangling all the qubits across a system at once — but since qubits can be error-prone, this raises the risk that the output of the simulation becomes meaningless noise.

Related: Google 'Willow' quantum chip has solved a problem the best supercomputer would have taken a quadrillion times the age of the universe to crack

The new approach to quantum simulation, outlined Feb. 5 in a study published in the journal Nature, takes advantage of both these options by blending digital and analog simulations into a single, multi-staged approach.

Simulation theory

This "hybrid" approach begins with a digital simulation layer, where scientists use the flexibility of the system to prepare the initial quantum states of each qubit pair— choosing the most pertinent position to start from. Next, the process switches to analog simulation, which can evolve toward the specific quantum states the scientists want to study.

Finally, the process switches back to a digital simulation to fine-tune and probe the quantum states to solve the most interesting problems in the physics being simulated.

The new research means that quantum computers will likely outperform conventional supercomputers in practical settings within the next five years, Hartmut Neven, the founder and lead of Google Quantum AI, said in an emailed statement. The time estimates vary greatly, with some suggesting this may be as far away as 20 years or achievable in the next couple.

Scientists have already demonstrated that Google's quantum computing chips, including Sycamore and the newly released Willow, can outperform the most powerful supercomputers — but so far only in benchmarking. To achieve supremacy in a practical scenario, the scientists said they must make further improvements in calibration and control accuracy, as well as improving the hardware. They also need to identify problems that both can be solved by quantum simulation and are too complex to address using classical computers.

However, the new hybrid research enables today's quantum computers to boost the capabilities of the fastest supercomputers. And this hybrid approach is already being harnessed to make new scientific discoveries, which the Google scientists achieved in testing their new approach. For example, in the behavior of magnets, the Google scientists addressed questions on how a magnet behaves when it's cooled to extremely low temperatures, and how energy flows from a hot to a cold part.

The hybrid approach was also used to show that the Kibble-Zurek mechanism (KZM) — a widely regarded model that predicts where defects form in a material — did not always hold true. Instead, the new hybrid simulation revealed entirely new physics. This is an example of the kind of discoveries that the hybrid approach quantum simulation can address, the scientists said.

A new type of autonomous quantum refrigerator could give quantum computers "a major performance boost" and make them more reliable, scientists say.

In a study published Jan. 9 in the journal Nature Physics, researchers successfully cooled a qubit to just 22 millikelvin (minus 459.63 degrees Fahrenheit, or minus 273.13 degrees Celsius) using a quantum refrigerator powered by "thermal baths" of microwave radiation. This is the lowest temperature that qubits have ever reached.

"This paves the way for more reliable and error-free quantum computations that require less hardware overload," study lead author Aamir Ali, research specialist in quantum technology at Chalmers University of Technology in Sweden, said in a statement.

Quantum computers need to be cooled to extremely low temperatures so scientists can tap into delicate quantum properties and perform calculations — as even the smallest environmental disturbance can "flip" their quantum state, causing errors. This is crucial for superconducting qubits — used in the likes of IBM's 1,000-qubit Condor chip — which need to operate at temperatures close to absolute zero (0 K, minus 459.67 F or minus 273.15 C) to maintain stability.

Cooling qubits to near absolute zero places them in their lowest possible energy state, otherwise known as their "ground state". In this state, qubits are likely to retain their quantum properties long enough to perform calculations accurately.

The new system complements conventional dilution refrigerators — which use helium gases to absorb heat through a dilution process and can bring qubits down to around 50 mK — by cooling qubits further, rather than replacing them altogether.

It does this by harnessing energy from reservoirs of heat created using microwave radiation, which is then directed into one of the quantum refrigerator's two qubits.

“Energy from the thermal environment, channeled through one of the quantum refrigerator’s two qubits, pumps heat from the target qubit into the quantum refrigerator’s second qubit, which is cold. That cold qubit is thermalized to a cold environment, into which the target qubit’s heat is ultimately dumped,” study co-author Nicole Yunger Halpern, adjunct assistant professor of physics and IPST at the University of Maryland, said in the statement.

Using this method, the scientists increased the likelihood that the qubit would be in its ground state before a computation to 99.97%.

Ali said this compares to probabilities between 99.8% and 99.92% achieved with previous techniques. "This might seem like a small difference, but when performing multiple computations, it compounds into a major performance boost in the efficiency of quantum computers," he added.

And unlike quantum dilution refrigerators, which are extremely complex and difficult to scale, the new, thermally-driven system is autonomous, meaning it doesn't require external control once started. The findings surpassed the researchers' initial expectations.

"Our work is arguably the first demonstration of an autonomous quantum thermal machine executing a practically useful task," study co-author Simone Gasparinetti, associate professor in quantum technology at Chalmers University of Technology, added in the statement. "We initially saw this as a proof of concept, so we were pleasantly surprised to find its performance surpasses all existing reset protocols for cooling qubits to record-low temperatures."

Quantum computers are expected to solve some problems beyond the reach of the most powerful supercomputers imaginable. Reaching this milestone has been dubbed "quantum supremacy."

But whether quantum supremacy has been achieved yet and what it would mean for the field remain unsettled.

The term "quantum supremacy" was coined in 2012 by John Preskill, a professor of theoretical physics at Caltech, to describe the point at which a quantum computer can do something that a classical one cannot.

Crossing this threshold has become a guiding star for the tech companies that are building large-scale quantum computers. In 2019, in a paper published in the journal Nature, Google became the first to declare it had achieved quantum supremacy. Other groups have made similar claims in recent years.

However, several of these assertions, including Google's, have since been rejected, after researchers developed novel classical algorithms that go toe-to-toe with quantum computers.

In addition, quantum supremacy experiments have focused on problems with no obvious practical applications, suggesting that useful quantum computers could still be some way off, William Fefferman, an assistant professor of computer science at the University of Chicago, told Live Science. Nonetheless, the idea has helped drive progress in the field and will be a crucial springboard toward more powerful machines, he added.

"You need to walk before you can run," Fefferman said. "I don't think anyone has a perfect road map for how to go from achieving quantum advantage in a really decisive way to this next step of solving a useful problem on a near-term quantum computer. But I'm convinced it's the first step in the process."

How quantum supremacy demonstrations have manifested so far

Theoretical computer scientists have discovered several quantum algorithms that can, in principle, solve problems much faster than classical ones. That’s because they can exploit quantum effects like entanglement and superposition to encode data very efficiently and process many more calculations in parallel than a classical computer can. But the number of qubits — the quantum equivalent of bits — required to implement them at sufficient scale to show an advantage is far beyond what's available with today's quantum processors.

As a result, efforts to demonstrate quantum supremacy have focused on highly contrived problems designed to favor the quantum computer. Google's 2019 experiment involved a 54-qubit processor carrying out a series of random operations. Although the output would be fundamentally useless, the researchers estimated that it would take roughly 10,000 years to simulate the process on Oak Ridge National Laboratory's Summit supercomputer, the most powerful classical machine in the world at the time.

That's because the unusual properties of quantum mechanics mean that simulating these systems on a classical computer quickly becomes intractable as they get larger, said Simon Benjamin, a professor of quantum technologies at the University of Oxford. "It's not that quantum computers are mysterious, magical things," he said. "We know the equations that they obey. But as you consider larger ones, it gets tougher and tougher for the classical computer to keep track of these equations."

This is due to the quantum phenomenon of superposition. Whereas a bit in a classical computer can represent only 1 or 0, a qubit can encode a complex mixture of both states at the same time. Crucially, multiple qubits can be in a shared superposition, meaning that a quantum system can represent all possible combinations of qubit values simultaneously.

That means that describing two qubits requires four numbers to cover all possible states of the system, Benjamin explained. And for each additional qubit, the number of classical bits required to represent the quantum computer's state doubles. "Pretty fast we find ourselves getting to big numbers," he said.

To provide an idea of how quickly the problem scales, Benjamin said, a 30-qubit system can be comfortably simulated on a good laptop. By 40 qubits, you would need a university-scale supercomputer, and by around 46 qubits, you'd reach the limits of the world's most powerful classical machines.

However, these estimates refer to the challenge of exactly simulating a perfect quantum system. In reality, today's quantum computers are highly error-prone, which provides shortcuts for classical algorithms. In 2022, a group from the Chinese Academy of Sciences showed that a university-scale supercomputer could simulate Google's 2019 quantum experiment in just hours, in part by sacrificing accuracy for speed.

Why quantum utility is favorable to quantum supremacy

Other quantum supremacy claims have met similar challenges. A group at the University of Science and Technology of China claimed in a 2021 paper that a random sampling operation they carried out on a 144-qubit light-based quantum computer would be beyond any classical machine. But Fefferman said his group has since shown that they can exploit the noise in the system to simulate the experiment in less than an hour. The same approach should be able to simulate a similar quantum supremacy experiment announced by startup Xanadu in 2022, he added.

As far as Fefferman knows, there are two quantum supremacy experiments still standing. In 2023, Google used a 70-qubit processor to extend the company's previous result, and in 2024, Quantinuum claimed to have crossed the milestone with its 56-qubit H2-1 quantum computer. But Fefferman wouldn't be surprised if classical approaches are developed that can quickly simulate these experiments in the future. "I'm not holding my breath," he said.

A definitive achievement of quantum supremacy will require either a significant reduction in quantum hardware's error rates or a better theoretical understanding of what kind of noise classical approaches can exploit to help simulate the behavior of error-prone quantum computers, Fefferman said.

But this back-and-forth between quantum and classical approaches is helping push the field forwards, he added, creating a virtuous cycle that is helping quantum hardware developers understand where they need to improve.

"Because of this cycle, the experiments have improved dramatically," Fefferman said. "And as a theorist coming up with these classical algorithms, I hope that eventually, I'm not able to do it anymore."

While it's uncertain whether quantum supremacy has already been reached, it's clear that we are on the cusp of it, Benjamin said. But it's important to remember that reaching this milestone would be a largely academic and symbolic achievement, as the problems being tackled are of no practical use.

"We're at that threshold, roughly speaking, but it isn't an interesting threshold, because on the other side of it, nothing magic happens," Benjamin said. "Quantum computers don't suddenly become useful."

That's why many in the field are refocusing their efforts on a new goal: demonstrating "quantum utility," or the ability to show a significant speedup over classical computers on a practically useful problem. Some groups, including researchers at IBM, are hopeful that even today's error-prone quantum computers could achieve this in the near term on some specific problems.

Google also recently demonstrated a key milestone in the race to achieve fault-tolerant quantum computing. Its "Willow" quantum processor was the first to remove more errors than were introduced as you scale up the number of physical qubits in a logical qubit. This means exponential error reduction and a possible pathway to error-free quantum computing.

But Benjamin said there is growing consensus in the field that this milestone won't be reached until we have fault-tolerant quantum computers. This will require quantum processors with many more qubits than we have today, he said, as the most well-studied quantum error-correction codes require on the order of 1,000 physical qubits to produce a single fault-tolerant, or logical, qubit.

With today's largest quantum computers having just crossed the 1,000-qubit mark, this is likely still some way off. "I'm optimistic that eventually such a quantum computer will exist, but I'm pessimistic that it will exist in the next five or 10 years," Fefferman said.

String theory is the most well known candidate for a theory of everything — a mathematical framework that would meld the world of the very small, described by quantum mechanics, and the very large, as described by Albert Einstein's general theory of relativity.

So far, these two theories do not agree with each other, and the problem comes from gravity. In an attempt to integrate gravity (which is weak at small scales where the other three fundamental forces are strong) string theory postulates that the universe is made up of tiny one-dimensional strings whose vibrations produce the particles we see.

The trouble is that many of string theory's predictions, such as there being an enormous array of possible universes, and the one we live in being a hologram projected from the edge of the universe, have thus far remained stubbornly untestable. This led Peter Woit, an arch critic of the theory, to accuse it of being "not even wrong."

But is his description fair? To discuss string theory, its implications for our universe, where it could be tested, and the contributions it has already made to math and science, we sat down with Marika Taylor at the HowTheLightGetsIn Festival in London. Taylor is Pro-Vice Chancellor and Head of College of Engineering and Physical Sciences at the University of Birmingham in the U.K., and her research focuses on using string theory and black hole observations to build a theory of quantum gravity. Here's what she had to say:

Ben Turner: What is string theory and why is it important?

Marika Taylor: String theory is a theory that unifies all the forces of nature, and would allow us to describe the force of gravity.

Why is that important? Well, I mean, first of all, you could say that humanity, since the beginning of time, has been trying to describe the natural world around us. That was what led people from early times to start writing down descriptions of the natural world. In a sense, this is the ultimate step, the theory of everything.

So there's human curiosity driving that. But there are lots of observations, physical phenomena, that we can't actually explain using existing theories. And so that drives us into creating an ultimate theory that explains everything.

BT: So what are the key postulates of string theory? And how does it differ from, say, general relativity?

MT: One basic postulate for us is that the theory needs to reduce to the known, successful theories in the areas [they apply]. So it has to reduce to Einstein's theory where Einstein's theory works really well.

But on a more fundamental level, I think some of the postulates would be that it is a theory in which there is predictable time evolution. So if you know the state of the universe at one time, that should uniquely determine the state of the universe at a later time.

Beyond that, it's hard to describe string theory because, in some sense, it isn't just one theory — it's actually a landscape. So in some regimes you might stack up postulates in terms of the actual behavior of strings. The fundamental postulate there is that every particle is actually a little string, and at different excitations the loops [of those strings] correspond to different particles.

BT: Why are there so many different string theories?

MT: It's because there are different ways to view the same physical phenomena. Over the last 20 to 30 years you would often hear the phrase duality [in the field]. That word reflects the fact that there are alternative descriptions of the same physical phenomena.

We used to think that the forces of gravity and particle physics were really conceptually different. Now we see that, actually, you might have the same kind of phenomena where, depending on the scale of the problem and the time steps you're looking at, they can be interchangeable.

BT: Soon after publishing his theory of general relativity, Einstein proposed three classic tests for his theory that scientists performed. Why haven't string theories produced similar tests?

MT: So I think this goes back to the question of where [we can find] a unified theory of gravity and particle physics. And the two key areas we need to look at are, firstly, the very early universe — ten to the [power of] minus 30 seconds — and secondly the surface and interior of black holes.

Most of the universe is well described just by the existing theories, so getting experimental evidence is so much harder now. But I also think it's important to remember that, almost immediately after he wrote it down, people realized that Einstein's theory predicted gravitational waves. But because they cause such small effects that make them hard to spot, they weren't detected until 100 years later.

BT: Some string theorists have remarked that to demonstrate the existence of a string, we would need to build a particle accelerator the size of a galaxy or greater. Are we really that far away? Or can we be smarter about where we look?

MT: Yes, I think it's about how clever we get at testing, because clearly no-one is going to build a particle accelerator that big.

Back when I was a student 28 years ago, people wouldn't have believed that we could get the level of accuracy of imaging of black hole surfaces [that we have]. So we shouldn't be looking to do this by a particle collider, we should be looking to the universe itself, because it's already doing those [particle] collisions for us.

Within the coming decades, we'll get more and more information about black holes colliding with each other. That's a really dramatic phenomenon. The collision between two black holes that was observed by the LIGO [Laser Interferometer Gravitational-Wave Observatory] detector (and for which the Nobel Prize was awarded) released three times the whole energy of the sun — not the energy that passes through us in a minute or a day, but the entire energy.

As we start getting more and more data of these mergers, and imaging them in more detail, that's the way we can look for interesting new physics.

BT: And, with the launch of LISA [Laser Interferometer Space Antenna], gravitational wave detectors are about to get a lot more sensitive. Will that help us to study mergers better?

MT: LISA is much more sensitive to gravitational waves produced in the early universe. With LIGO, you wouldn't see them because they're at the wrong wavelengths. So it will be interesting.

LISA will also see a lot more detail about the super heavy black holes in the center of galaxies. Those are associated with the seeds from which galaxies first formed. So again, it will give us a lot more information.

BT: Besides black holes, you mentioned there might be signs in the early universe too. What can we look for there?

MT: Well, people hope that in the cosmic microwave background, which has been imaged to really very high precision, there might be some smoking gun signals for string theory effects.

That doesn't seem to be the case. It could have been that there were features coming from particles existing in certain groups according to string theory. They calculated these and found that the effects were likely too small to be seen in the microwave background.

But there are other ways to observe cosmology — the microwave background is just a snapshot, one moment in time. People are interested in measuring other things. There's 21 centimeter cosmology [the 21cm line of redshifted atomic hydrogen] that you can measure over a series of times. It's not just a snapshot, it's like a movie. That could potentially contain more information that lets us pin down the next generations of experiments.

BT: Part of your research is in looking at how black holes behave similarly to quantum computers. For a layperson, that could seem like a giant conceptual leap. How are the two connected?

MT: The details of how this works are certainly still under study. But a black hole behaves like a very efficient quantum computer. If you throw something into it, that object is stored inside the black hole as if it's on a quantum computer's hard drive. And the evaporation of a black hole is akin to doing a quantum computing process.

One should think of the surface of a black hole as being like quantum computer smart disks. People find that hard to conceptualize, because we're so used to seeing computer hard disks as flat objects, we don't want to see them as big spherical ones. But really just think that you're storing information on that surface. And, as I throw something into a black hole, that actually gets imprinted on a hard disk. It's like doing an operation.

BT: So if someone were to fall into a black hole, they would be stretched until they ripped apart, then they'd be filed away on those qubits?

MT: Yeah, that's right.

BT: It's a unique way to go. We touched on this earlier with gravitational wave detectors, but how long will it be before we get some fundamental advances on the experimental side of all this?

MT: I think it depends on whether you want something that's a smoking gun of the whole theory, or whether you want to explore aspects of it.

Certainly people do experiments which uncover some things you just hadn't thought about, [such as] the ways that black holes behave as quantum computers. In holography, black holes are described by theories that don't have gravity. You can actually simulate those in the lab.

On the bigger question, say we want to know the shape of the extra dimensions of string theory, the timescale on which we can do experiments is obviously longer. But I think the onus is on the theorists to get clever about that.

I also tie it to the big theories that we have, such as the cosmological constant or dark energy. If you can ultimately say that string theory predicts something like dark energy, can we then go and predict string theory through that? Because we've got no other explanation for dark energy.

I'm very careful, and I don't think people should overpromise. But I think that just because you can't experimentally measure it, it doesn't mean that people can't study it.

BT: Say we woke up tomorrow and there was smoking gun evidence that string theory was wrong. Are there any other alternative theories you find compelling? Or is it really the dominant one?

MT: String theory is a collection of ideas of fundamental physics. I think it's very unlikely that there'd be a smoking gun saying that all of it was wrong. It would say some aspects of it were wrong, and then you focus on the bits that are left over.

I think it's really important to explore ideas in all different directions. But as for alternate theories of quantum gravity, there's no real competitor.

Editor's note: This interview has been edited and condensed for clarity.

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Scientists say they have cracked a decade-old problem that could bring the concept of a "quantum hard drive" closer to reality.

The solution involved developing a new type of error-correction system for stabilizing qubits — the building blocks of quantum information — against interference, overcoming a major hurdle facing the development of practical quantum computers.

If successfully scaled, the technique could pave the way for highly efficient quantum memory systems capable of storing huge volumes of quantum data, researchers claimed in a new study published Nov. 4 in the journal Nature Communications.

"This advance is crucial for the development of scalable quantum computers, as it allows for a more compact construction of quantum memory systems," the researchers said in a statement. "By reducing the physical qubit overhead, the findings pave the way for the creation of a more compact 'quantum hard drive' — an efficient quantum memory system capable of storing vast amounts of quantum information reliably."

Related: Will we ever have quantum laptops?

One of the biggest challenges in quantum computing lies in managing errors that disrupt calculations.

Quantum computers rely on qubits, tiny units of quantum information akin to bits in classical computers, that are incredibly sensitive to environmental disturbances like temperature changes and electromagnetic interference. Even minuscule disruptions to a qubit’s delicate quantum state can result in lost data and errors in quantum systems.

For years, researchers have worked on ways to keep these qubits, and the quantum data they hold, stable. Error correction in quantum systems is typically achieved by organizing qubits in a lattice structure that follows a topological "code." The aim is to win an "arms race" by using as few physical qubits as possible to manage errors as they arise, the researchers explained

However, current 3D error-correction methods can only handle errors along a single line of qubits, limiting how much error they can manage as the system grows. The researchers overcame this problem by developing an error-correction architecture that uses a 3D lattice of qubits organized by a topological code that enables errors to be corrected across two-dimensional surfaces within the 3D structure, rather than just in a single dimension.

This structure can handle more errors as the system grows by correcting them over broader, two-dimensional surfaces within the 3D lattice, allowing it to scale more efficiently, the researchers said

"There remain significant barriers to overcome in the development of a universal quantum computer. One of the biggest is that we need to use most of the qubits — quantum switches at the heart of the machines — to suppress the errors that emerge as a matter of course within the technology," lead author Dominic Williamson, researcher at the University of Sydney Nano Institute and School of Physics, said in the statement.

"Our proposed quantum architecture will require fewer qubits to suppress more errors, liberating more for useful quantum processing."

Prof. Stephen Bartlett, quantum theorist and director of the University of Sydney Nano Institute, added in the statement: "This advancement could help transform the way quantum computers are built and operated, making them more accessible and practical for a wide range of applications, from cryptography to complex simulations of quantum many-body systems."

D-Wave has completed calibrating and benchmarking its latest processor — a 4,400-plus-qubit behemoth that it claims is 25,000 times faster than its predecessor.

The Advantage2 quantum processing unit (QPU) is designed for complex applications including artificial intelligence (AI), materials science and optimization tasks. In a statement issued Nov. 6, D-Wave representatives said the new chip demonstrated "substantial performance gains" over its existing 5,000-qubit Advantage device, including improved speed and accuracy.

"Recent performance benchmarks demonstrate that the 4,400+ qubit Advantage2 processor is computationally more powerful than the current Advantage system, solving a range of problems — including 3D lattice problems common in materials science — 25,000 times faster," the company said in the statement. "The processor also delivers five times better solutions on problems requiring a high degree of precision. Furthermore, it surpasses the current Advantage system in 99% of tests on satisfiability problems, highlighting its capabilities across a wide range of quantum applications."

3D lattice problems are often used in materials science to model atomic interactions. Faster solutions mean researchers can conduct these simulations more quickly, which supports faster development and testing of new materials.

Related: Radical quantum computing theory could lead to more powerful machines than previously imagined

Boolean satisfiability (SAT) problems, meanwhile, are benchmarks that assess a system’s ability to handle complex decision-making tasks with multiple possible solutions. These tests help gauge the processor’s efficiency in applications like cryptography and logistics, where quickly finding solutions that satisfy multiple rules or conditions is essential.

As well as performance upgrades, D-Wave said its new processor delivers improvements in three key areas: coherence time, energy scale and qubit connectivity.

Coherence time refers to how long qubits — the building blocks of quantum information — can maintain their quantum state without interference. A longer coherence time allows for more stable and accurate calculations, improving the reliability of quantum computations. D-Wave reported that its new chip offers double the coherence time of its previous system.

The Advantage2 also provides a 40% increase in energy scale, company representatives said in the statement, enabling the chip to handle more complex calculations with improved stability. Finally, qubit connectivity — the number of connections each qubit can make with other qubits — has been boosted from 15-way to 20-way, enabling the Advantage2 to tackle larger and more intricate problems than its predecessor.

"Our strategic decision to focus development efforts on enhancing the connectivity and coherence of our next annealing quantum computing system has proven successful," Trevor Lanting, chief development officer at D-Wave, said in the statement. "We’re thrilled with the performance of our recently calibrated processor, and we believe this technology will deliver amazing results for our customers, solving bigger and more complex problems."

IBM's latest quantum computer is now powerful enough for useful scientific research, scientists say, after the company made significant hardware and software improvements to its quantum system.

The new system is made of two parts: a new 156-qubit quantum processing unit (QPU) called R2 IBM Heron (the second generation of a chip launched last year); and Qiskit — a collection of software tools and algorithms designed to optimize quantum computing performance.

The result is a new system that can perform tasks up to 50 times faster than previous efforts, according to benchmarking data. For reference, in IBM's 2023 quantum utility experiment, published in the journal Nature, its most powerful quantum computer at the time took 122 hours to run workloads in the benchmark. The new system, fitted with the R2 Heron QPU, took just 2.4 hours.

The new quantum computers, based in IBM's data centers based around the world can tackle scientific problems across materials, chemistry, life sciences, high-energy physics and more domains, IBM representatives said in a statement.

Related: New 'gold-plated' superconductor could be the foundation for massively scaled-up quantum computers in the future

"Advances across IBM Quantum hardware and Qiskit are enabling our users to build new algorithms in which advanced quantum and classical supercomputing resources can be knit together to combine their respective strengths," Jay Gambetta, vice president for IBM Quantum, said in the statement.

Next-generation quantum processing

The R2 Heron QPU is fitted with 156 qubits arranged in a heavy-hexagonal lattice — a topological structure that IBM uses for all its quantum processors. This enables the system to reliably execute quantum circuits of up to 5,000 two-qubit gates — which is nearly double the 2,880 two-qubit gates in the 2023 utility experiment, powered by the 127-qubit Eagle QPU.

Two-qubit gates are essential to unlocking the exponential power of a quantum computer — in which the more qubits there are fitted into a system, the more calculations can run in parallel. Single qubit gates allow for individual qubits to rotate or flip their states, while two-qubit gates operating in pairs of qubits tap into the laws of quantum mechanics to enable entanglement between them. While single-qubit gates can function on a basic level, utilizing two-qubit gates can enable a quantum computer to perform far more complex calculations.

The new R2 Heron chip also features "two-level system mitigation," which helps to reduce the impact of disturbances to the qubits interacting with the materials surrounding them. The system also benefits from software improvements to error correction — namely, the use of Qiskit's tensor error network mitigation algorithm (TEM).

Further software improvements, including the launch of the latest generation of the runtime engine, optimizing data movement and the introduction of parametric compiling, mean the new system can run at 150,000 circuit layer operations per second (CLOPS). In comparison, base performance was just 950 CLOPS in 2022 and 37,000 CLOPS earlier this year when optimizing data movement was first introduced.

Quantum-centric supercomputing

IBM representatives claim that the latest developments feed into their vision of developing "quantum-centric" supercomputers — which combine quantum and classical computers to achieve viable results sooner than they would by using only quantum computers. .

This is because hybrid systems can address workloads in parallel, breaking down complex algorithms by assigning parts of the task to the half of the system for which they are best suited. Once these chunks are solved, the software layer seamlessly stitches the problems back together.

An example of quantum-centric supercomputing in action is at RIKEN, a scientific research center in Japan. Using a method known as "Quantum-Selected Configuration Interaction," outlined in a paper published to the arXiv preprint database in 2023, scientists are using quantum hardware to model the electronic structure of iron sulfides.

Scientists at RIKEN have also embarked on a project to build a quantum-high-performance-computing hybrid platform by integrating Fugaku, one of the world's fastest supercomputers, with an on-premises IBM System Two quantum computer powered by the Heron QPU.

Technology companies are pouring billions of dollars into quantum computing, despite the technology still being years away from practical applications. So what will future quantum computers be used for — and why are so many experts convinced they will be game-changing?

Building a computer that harnesses the unusual properties of quantum mechanics is an idea that has been in contention since the 1980s. But in the last couple of decades, scientists have made significant strides in building large-scale devices. Now, a host of tech giants from Google to IBM as well as several well-funded startups have invested significant sums into the technology — and they have created several individual machines and quantum processing units (QPUs).

In theory, quantum computers could solve problems that are beyond even the most powerful classical computer. However, there’s broad consensus that such devices will need to become much larger and more reliable before that can happen. Once they do, however, there is hope that the technology will crack a host of currently unsolvable challenges in chemistry, physics, materials science and even machine learning.

"It's not just like a fast classical computer, this is a completely different paradigm,” Norbert Lütkenhaus, executive director of the Institute for Quantum Computing at the University of Waterloo in Canada told Live Science. "Quantum computers can solve some tasks efficiently that classical computers simply cannot do."

The current state-of-the-art

The most fundamental building block of a quantum computer is the qubit — a unit of quantum information that is comparable to a bit in a classical computer, but with the uncanny ability to represent a complex combination of both 0 and 1 simultaneously. Qubits can be implemented on a wide range of different hardware, including superconducting circuits, trapped ions or even photons (light particles).

Today’s largest quantum computers have just crossed the 1,000 qubit mark, but most feature just a few tens or hundreds of qubits. They are far more error-prone than classical computing components due to the extreme sensitivity of quantum states to external noise, which includes temperature changes or stray electromagnetic fields. That means that it's currently difficult to run large quantum programs for long enough to solve practical problems.

Related: Radical quantum computing theory could lead to more powerful machines than previously imagined

That doesn’t mean today’s quantum computers are useless, though, said William Oliver, director of the Center for Quantum Engineering at the Massachusetts Institute of Technology (MIT) in the USA. "What quantum computers are used for today is basically to learn how to make quantum computers bigger, and also to learn how to use quantum computers," he said in an interview with Live Science.

Building ever larger processors provides crucial insight into how to engineer larger, more reliable quantum machines and provides a platform to develop and test novel quantum algorithms. They also let researchers test quantum error-correction schemes, which will be crucial for achieving the technology’s full promise. These typically involve spreading quantum information over multiple physical qubits to create a single "logical qubit," which is far more resilient.

Lütkenhaus said that recent breakthroughs in this area suggest fault-tolerant quantum computing might not be so far off. Several companies including QuEra, Quantinuum and Google have recently demonstrated the ability to generate logical qubits reliably. Scaling up to the thousands, if not millions, of qubits that we need to solve practical problems will take time and a lot of engineering effort, says Lütkenhaus. But once that’s been achieved, a host of exciting applications will come into view.

Where quantum could be a game changer

The secret to quantum computing’s power lies in a quantum phenomenon known as superposition, said Oliver. This allows a quantum system to occupy multiple states simultaneously until it is measured. In a quantum computer, this makes it possible to place the underlying qubits into a superposition representing all potential solutions to a problem.

"As we run the algorithm, the answers that are incorrect are suppressed and the answers that are correct are enhanced," said Oliver. "And so by the end of the calculation, the only surviving answer is the one that we're looking for."

This makes it possible to tackle problems too vast to work through sequentially, as a classical computer would have to, Oliver added. And in certain domains, quantum computers could carry out calculations exponentially faster than their classical cousins as the size of the problem grows.

One of the most obvious applications lies in simulating physical systems, said Oliver, because the world itself is governed by the principles of quantum mechanics. The same strange phenomena that make quantum computers so powerful also make simulating many quantum systems on a classical computer intractable at useful scales. But because they operate on the same principles, quantum computers should be able to model the behavior of a wide range of quantum systems efficiently.

This could have a profound impact on areas like chemistry and materials science where quantum effects play a major role, and could lead to breakthroughs in everything from battery technology to superconductors, catalysts and even pharmaceuticals.

Quantum computers also have some less savory uses. Given enough qubits, an algorithm invented by mathematician Peter Shor in 1994 could crack the encryption that underpins much of today’s internet. Fortunately, researchers have devised new encryption schemes that sidestep this risk, and earlier this year the US National Institute of Standards and Technology (NIST) released new "post-quantum" encryption standards that are already being implemented.

Emerging possibilities of quantum computing

Other applications for quantum computers are, at present, somewhat speculative, said Oliver.

There are hopes the technology could prove useful for optimization, which involves searching for the best solution to a problem with many possible solutions. Lots of practical challenges can be boiled down to optimization processes, from easing traffic flows through a city to finding the best delivery routes for a logistics company. Building the best portfolio of stocks for a specific financial goal could also be a possible application.

So far, though, most quantum optimization algorithms offer less than exponential speed-ups. Because quantum hardware operates much slower than current transistor-based electronics, these modest algorithmic speed advantages can quickly disappear when implemented on a real-world device.

At the same time, progress in quantum algorithms has spurred innovations in classical computing. "As quantum algorithm designers come up with different optimization schemes, our colleagues in computer science advance their algorithms and this advantage that we seem to have ends up evaporating," added Oliver.

Other areas of active research with less clear long-term potential include using quantum computers to search large databases or conduct machine learning, which involves analyzing large amounts of data to discover useful patterns. Speed-ups here are also less than exponential and there is the added problem of translating large amounts of classical data into quantum states that the algorithm can operate on — a slow process that can quickly eat into any computational advantage.

But it is still early days, and there is plenty of scope for algorithmic breakthroughs, said Oliver. The field is still in the process of discovering and developing the building blocks of quantum algorithms — smaller mathematical procedures known as "primitives" that can be combined to solve more complex problems.

"We need to understand how to build quantum algorithms, identify and leverage these program elements, find new ones if they exist, and understand how to put them together to make new algorithms," says Oliver.

This should guide the future development of the field, added Lütkenhaus, and is something companies should bear in mind when making investment decisions. "As we push the field forward, don't focus too early on very specific problems," he said. "We still need to solve many more generic problems and then this can branch off into many applications."

Researchers in China say they've used a quantum computer to break RSA encryption. But that doesn't necessarily mean your emails or WhatsApp messages will be intercepted anytime soon.

Encryption is used to protect sensitive data, like banking information and medical records, when it is transmitted over the internet. RSA — named after its creators, Ron Rivest, Adi Shamir and Leonard Adleman — is a type of encryption, called asymmetric encryption, which uses two different-but-linked keys to solve a mathematical problem.

Encryption has proved to be a successful method for protecting sensitive information, as it requires mathematical computation so complex that it cannot be solved by even the most powerful supercomputers in the world today — unless they have the cryptographic key.

It has long been predicted that quantum computers would make current encryption technology obsolete. Quantum computers can process vast amounts of information in far less time than a conventional computer can. This is because, thanks to the laws of quantum mechanics — and the qubits that power them — they can process calculations in parallel rather than in sequence. In theory, this means that it will take a quantum computer just seconds to solve a problem that would take classical computers millions of years.

Related: Future quantum computers will be no match for 'space encryption' that uses light to beam data around — with the 1st satellite launching in 2025

Quantum computing is a nascent technology, however, and the most powerful quantum machines today have thousands of qubits. And scientists have projected we will need a machine with millions of qubits for it to be more powerful than our most powerful classical computers. Quantum computers also require dedicated laboratories, as well as expensive and complicated infrastructure.

But in a study published in the journal Chinese Journal of Computers in May, researchers found that D-Wave Advantage — a 5,760-qubit machine created by California-based D-Wave Quantum Systems — could break the RSA encryptions they challenged it to solve.

The machine did this through a process called quantum annealing. Quantum annealing uses quantum fluctuations — erratic changes in energy levels in quantum systems — to optimize a problem so it is solved in the easiest way possible.

Although they used a quantum computer to decrypt an RSA encryption, they used only a 50-bit integer for the RSA encryption. Size really does matter in encryption. The strength of an RSA encryption relates to the length of the integer — which defines how big the problem is. For example, a 50-bit integer has 9.67 x 10^16 possible values.

But most modern encryption technologies now use 1024- to 2048-bit integers. A 1024-bit integer has 1.797 x 10^308 possible values, while a 2048-bit integer has 3.231 x 10^616 possible values. Hence, the number of possible values for modern encryption methods are immensely larger — and, therefore, more complex — than the one overcome by the researchers.

The research is an interesting proof of concept that reinforces the expectation that quantum computers can one day decrypt modern encryption technologies. Although not stated in the paper, the natural next steps for research like this will examine how D-Wave Advantage and quantum annealing can cope with encryption models with larger integers, such as 128- or 256-bit integers.

It also signals that quantum computers are coming and will have an impact on security that relies on encryption. That is why scientists are also building post-quantum cryptography technologies — a type of cryptography that uses algorithms that are resistant to being solved by quantum computers. However, like quantum computers, this technology is still years away from full realization.

Computers that exploit the weird rules of quantum mechanics may soon crack problems that are unsolvable using existing technology. Today’s machines are still far from achieving that, but the field of quantum computing has made dramatic progress since its inception.

Quantum computing has gone from an academic curiosity to a multi-billion-dollar industry in less than half a century and shows no signs of stopping. Here are 12 of the most important milestones on that journey.

1980: The quantum computer is born

By the 1970s, scientists had begun thinking about potential crossovers between the new fields of quantum mechanics and information theory. But it was American physicist Paul Benioff who crystallized many of these ideas when he published the first-ever description of a quantum computer. He proposed a quantum version of a "Turing machine" — a theoretical model of a computer, devised by renowned British computer scientist Alan Turing, that is capable of implementing any algorithm. By showing that such a device could be described using the equations of quantum mechanics, Benioff laid the foundations for the new field of quantum computing.

1981: Richard Feynman popularizes quantum computing

Both Benioff and legendary physicist Richard Feynman gave talks on quantum computing at the first Physics of Computation Conference in 1981. Feynman’s keynote speech was on the topic of using computers to simulate physics. He pointed out that because the physical world is quantum in nature, simulating it exactly requires computers that similarly operate based on the rules of quantum mechanics. He introduced the concept of a "quantum simulator," which cannot implement any program like a Turing machine, but can be used to simulate quantum mechanical phenomena. The talk is often credited for kick-starting interest in quantum computing as a discipline.

1985: The "universal quantum computer"

One of the foundational concepts in computer science is the idea of the universal Turing machine. Introduced by its namesake in 1936, this is a particular kind of Turing machine that can simulate the behavior of any other Turing machine, allowing it to solve any problem that is computable. However, David Deutsch, a professor in the quantum theory of computation, pointed out in a 1985 paper that because the universal computer described by Turing relied on classical physics, it would be unable to simulate a quantum computer. He reformulated Turing’s work using quantum mechanics to devise a “universal quantum computer,” which is capable of simulating any physical process.

1994: First killer use case for quantum computers

Despite the theoretical promise of quantum computers, researchers had yet to find clear practical applications for the technology. American mathematician Peter Shor became the first to do so when he introduced a quantum algorithm that could efficiently factorize large numbers. Factorization is the process of finding the smallest set of numbers that can be combined to create a larger one. This process becomes increasingly difficult for larger numbers and is the basis for many leading encryption schemes. Shor’s algorithm can solve these problems exponentially faster than classical computers, though, raising fears that quantum computers could be used to crack modern encryption and spurring the development of post-quantum cryptography.

1996: Quantum computing takes on search

It didn’t take long for another promising application to appear. Bell Labs computer scientist Lov Grover proposed a quantum algorithm for unstructured search, which refers to looking for information in databases with no obvious system of organization. This is like looking for the proverbial needle in a haystack and is a common problem in computer science, but even the best classical search algorithms can be slow when faced with large amounts of data. The Grover algorithm, as it has become known, exploits the quantum phenomenon of superposition to dramatically speed up the search process.

1998: First demonstration of a quantum algorithm

Dreaming up quantum algorithms on a blackboard is one thing, but actually implementing them on hardware had proven much harder. In 1998, a team led by IBM researcher Isaac Chuang made a breakthrough when they showed that they could run Grover’s algorithm on a computer featuring two qubits — the quantum equivalent of bits. Just three years later Chuang also led the first implementation of Shor’s algorithm on quantum hardware, factoring the number 15 using a seven-qubit processor.

1999: The birth of the superconducting quantum computer

The fundamental building blocks of a quantum computer, known as qubits, can be implemented on a wide range of different physical systems. But in 1999, physicists at Japanese technology company NEC hit upon an approach that would go on to become the most popular approach to quantum computing today. In a paper in Nature, they showed that they could use superconducting circuits to create qubits, and that they could control these qubits electronically. Superconducting qubits are now used by many of the leading quantum computing companies, including Google and IBM.

2011: First commercial quantum computer released

Despite considerable progress, quantum computing was still primarily an academic discipline. The launch of the first commercially available quantum computer by Canadian company D-Wave in May 2011 heralded the start of the quantum computing industry. The start-up’s D-Wave One featured 128 superconducting qubits and cost roughly $10 million. However, the device wasn’t a universal quantum computer. It used an approach known as quantum annealing to solve a specific kind of optimization problem, and there was little evidence it provided any speed boost compared to classical approaches.

2016: IBM makes quantum computer available over the cloud

While several large technology companies were developing universal quantum computers in-house, most academics and aspiring quantum developers had no way to experiment with the technology. In May 2016, IBM made its five-qubit processor available over the cloud for the first time, allowing people from outside the company to run quantum computing jobs on its hardware. Within two weeks more than 17,000 people had registered for the company’s IBM Quantum Experience service, giving many their first hands-on experience with a quantum computer.

2019: Google claims "quantum supremacy"

Despite theoretical promises of massive "speedup," nobody had yet demonstrated that a quantum processor could solve a problem faster than a classical computer. But in September 2019, news emerged that Google had used 53 qubits to perform a calculation in 200 seconds that it claimed would take a supercomputer roughly 10,000 years to complete. The problem in question had no practical use: Google’s processor simply performed random operations and then researchers calculated how long it would take to simulate this on a classical computer. But the result was hailed as the first example of "quantum supremacy," now more commonly referred to as "quantum advantage."

2022: A classical algorithm punctures supremacy claim

Google’s claim of quantum supremacy was met with skepticism from some corners, in particular from arch-rival IBM, which claimed the speedup was overstated. A group from the Chinese Academy of Sciences and other institutions eventually showed that this was the case, by devising a classical algorithm that could simulate Google’s quantum operations in just 15 hours on 512 GPU chips. They claimed that with access to one of the world’s largest supercomputers, they could have done it in seconds. The news was a reminder that classical computing still has plenty of room for improvement, so quantum advantage is likely to remain a moving target.

2023: QuEra smashes record for most logical qubits

One of the biggest barriers for today’s quantum computers is that the underlying hardware is highly error-prone. Due to the quirks of quantum mechanics, fixing those errors is tricky and it has long been known that it will take many physical qubits to create so-called “logical qubits” that are immune from errors and able to carry out operations reliably. Last December, Harvard researchers working with start-up QuEra smashed records by generating 48 logical qubits at once – 10 times more than anyone had previously achieved. The team was able to run algorithms on these logical qubits, marking a major milestone on the road to fault-tolerant quantum computing.

Physicists have created a new model for quantum computers that could more easily scale them up and make them more powerful than previously imagined. 

The new theory, outlined in a study published May 21 in the journal PRX Quantum, proposes linking qubits, the fundamental workhorses of quantum computers, over vast distances to work as if they were part of a single super-powerful machine. 

Where bits are used in classical computing to process data in binary states of 1 or 0, and in sequence, quantum computing uses qubits (which rely on the laws of quantum mechanics) to encode data in a superposition of 1 and 0. This means data can be encoded in both states simultaneously. Each qubit operates in a given frequency.

These qubits can then be stitched together through quantum entanglement — where their data is linked across vast separations over time or space — to process calculations in parallel. The more qubits are entangled, the more exponentially powerful a quantum computer will become. 

Entangled qubits must share the same frequency. But the study proposes giving them "extra" operating frequencies so they can resonate with other qubits or work on their own if needed.

The road to quantum supremacy

With enough entangled qubits, future quantum computers could perform calculations that would have taken a classical computer thousands of years in just a few seconds. But you need a quantum processor with millions of qubits to achieve this state of "quantum supremacy," whereas the most powerful today have just 1,000 qubits.

But maintaining the stability between entangled qubits, so that you can process data, is difficult and requires complex electronics and equipment. Scaling up the qubits in a quantum computer so it's powerful enough to leapfrog today's most powerful supercomputers also represents a major hurdle — as you would also need to scale up that complex circuitry.

Related: Quantum compasses closer to replacing GPS after scientists squeeze key refrigerator-sized laser system onto a microchip

But the scientists propose that by giving each qubit extra frequencies, they can get them to work together to process calculations as if they were a part of a single quantum computer. This is despite being potentially separated over vast distances. It means that instead of one massive quantum processor that is difficult to maintain, you can use several smaller ones linked together.

“Each qubit in a quantum computer operates at a specific frequency. Realizing the capabilities unique to a quantum computer relies on being able to control each qubit individually via a distinct frequency, as well as to link pairs of qubits by matching their frequencies,” study lead author Vanita Srinivasa, assistant professor of quantum information at the University of Rhode Island, said in a statement. 

Fitting qubits together like 'LEGO blocks'

The scientists said that by applying oscillating voltages, they could generate extra frequencies for each qubit. By doing this, you can link multiple qubits together by tapping into newly generated shared frequencies, without having to match their original frequencies. The qubits can then be linked together, yet also be controlled individually using their original frequencies.

“This approach to scaling is like building a larger system using fixed-size LEGO blocks, which are like individual modules, and connecting them using longer pieces that are strong enough to maintain the connection between the blocks for a sufficient time before external influences break the links,” Srinivasa said in the statement. 

The model aims to overcome challenges that scientists will face in scaling up quantum processors in the future. These are normally fabricated with semiconductors and use billions of tiny transistors that can be harnessed to make compact qubits. However, simply adding more and more qubits to a quantum processor will one day be infeasible, the scientists said.

Using the new model, the researchers believe future quantum computers will be built in a modular way — with smaller arrays of qubits in quantum processors that are connected using robust and long-range entangled links. This will render them more powerful and capable of much faster calculations than is feasible using the technology that we have today. 

A new quantum computer has broken a world record in "quantum supremacy," topping the performance of benchmarking set by Google's Sycamore machine by 100-fold. 

Using the new 56-qubit H2-1 computer, scientists at quantum computing company Quantinuum ran various experiments to benchmark the machine's performance levels and the quality of the qubits used. They published their results June 4 in a study uploaded to the preprint database arXiv. The study has not been peer-reviewed yet.

To demonstrate the potential of the quantum computer, the scientists at Quantinuum used a well-known algorithm to measure how noisy, or error-prone, qubits were. 

Quantum computers can perform calculations in parallel thanks to the laws of quantum mechanics and entanglement between qubits, meaning the fates of different qubits can instantly change each other. Classical computers, by contrast, can work only in sequence. 

Adding more qubits to a system also scales up the power of a machine exponentially; scientists predict that quantum computers will one day perform complex calculations in seconds that a classical supercomputer would have taken thousands of years to solve. 

The point where quantum computers overtake classical ones is known as "quantum supremacy," but achieving this milestone in a practical way would need a quantum computer with millions of qubits. The largest machine today has only about 1,000 qubits. 

Related: Quantum computing breakthrough could happen with just hundreds, not millions, of qubits using new error-correction system

The reason we would need so many qubits for "quantum supremacy" is that they are inherently prone to error, so many would be needed to correct those errors. That's why many researchers are now focusing on building more reliable qubits, rather than simply adding more qubits to machines. 

The team tested the fidelity of H2-1's output using what's known as the linear cross entropy benchmark (XEB). XEB spits out results between 0 (none of the output is error-free) and 1 (completely error-free), Quantinuum representatives said in a statement. 

Scientists at Google first tested the company's Sycamore quantum computer using XEB in 2019, demonstrating that it could complete a calculation in 200 seconds that would have taken the most powerful supercomputer at the time 10,000 years to finish. They registered an XEB result of approximately 0.002 with the 53 superconducting qubits built into Sycamore. 

But in the new study, Quantinuum scientists — in partnership with JPMorgan, Caltech and Argonne National Laboratory — achieved an XEB score of approximately 0.35. This means the H2 quantum computer can produce results without producing an error 35% of the time. 

"We are entirely focused on the path to universal fault tolerant quantum computers," Ilyas Khan, chief product officer at Quantinuum and founder of Cambridge Quantum Computing, said in the statement. "This objective has not changed, but what has changed in the past few months is clear evidence of the advances that have been made possible due to the work and the investment that has been made over many, many years." 

Quantinuum previously collaborated with Microsoft to demonstrate "logical qubits" that had an error rate 800 times lower than physical qubits. 

In the study, published in April, scientists demonstrated they could run experiments with the logical qubits with an error rate of just 1 in 100,000 — which is much stronger than the 1-in-100 error rate of physical qubits, Microsoft representatives said.

"These results show that whilst the full benefits of fault tolerant quantum computers have not changed in nature, they may be reachable earlier than was originally expected," added Khan.

A new quantum computing study claims that a recent finding in the production, storage and retrieval of "quantum data" has brought us one step closer to the quantum internet.

Currently, quantum information is unstable over long distances and quantum bits, or qubits — the carriers of quantum information — are easily lost or fragmented during transmission.

Classical computer bits are transmitted today as pulses of light through fiber optic cables using devices called "repeaters" to amplify signals across the length of the network. To transmit qubits over longer distances the way classical computer bits are transmitted today we need similar devices that can store and retransmit quantum states across the whole network, ensuring signal fidelity no matter how far the data has to go.

These quantum memory devices could receive, store and retransmit qubit states. The new study, conducted at Imperial College London, the University of Southampton, and the Universities of Stuttgart and Wurzburg in Germany, claims to have achieved this using standard fiber optic cables for the first time. The findings were published April 12 in the journal Scientific Advances.

All in the photon source

The researchers stored and retrieved photons — one of the potential carriers of quantum information — using a new and potentially much more efficient method.

"There are two main types of single photon sources,a process called non-linear optical frequency conversion and those based on single emitters," Sarah Thomas, professor of physics at Imperial College, London, told Live Science. "It's been demonstrated many times before that we can store photons from nonlinear optics in a quantum memory because you can engineer the source and memory to match. We used a particular single emitter called a quantum dot, which is a nanocrystal of semiconductors."

Thomas said that using nonlinear optics is less reliable — a pair of usable photons isn't produced every time, whereas a single emitter quantum dot produces them at a higher rate.

Related: Bizarre device uses 'blind quantum computing' to let you access quantum computers from home

The next challenge is that the efficiency of the interface between quantum memory devices depends on matching both the wavelength and bandwidth. Discrepancies here make storage and retrieval too inefficient, but the study finally bridged the gap.

"We did it by using a high-bandwidth, low-noise quantum memory, fabricating the photon source at a very specific wavelength to match our quantum memory," Thomas said. "We were also able to do it at a wavelength where the loss in optical fiber is the lowest, which will be key in the future for building quantum networks."

Building on past work

But this is not the only recent advance in quantum computing and the quantum internet. In February, Live Science reported on a related breakthrough at Stony Brook University.

Quantum network models are more stable at extremely low temperatures, which limits their real-world applications, but the study achieved a stable connection at room temperature, which puts it within reach of real-world use.

The Imperial study builds on that success thanks to the aligned wavelengths between transmitter and receiver.

"The Stony Brook study used photons at 795 nm [nanometers] and showed interference of two photons after storage and retrieval," Mark Saffman, chief scientist for quantum information at quantum-enabled products company Infleqtion told Live Science. "The Imperial study used a photon at 1529 nm (which is the standard telecom wavelength) and stored and retrieved it, but didn't show interference. The storage and retrieval of telecom wavelength is important for low-loss fiber transmission. Both studies advance different aspects of what's needed for a quantum network."

Michael Hasse, a cybersecurity expert (one of the areas where quantum networks will have the most impact) told Live Science that the Imperial study describes a method whereas the earlier study describes a mechanism necessary for that method to work.

"The Imperial work is about a means of establishing long-distance communication using repeaters," he said. "Quantum entanglement allows communications to be far apart in theory, but in reality it's easier when they're closer together. The Stony Brook study refers to the storage of quantum information at room temperature, which is necessary for cost-effective implementation of repeaters."

Scientists in China have developed a 504-qubit quantum computing chip that will be made available to researchers worldwide via a new quantum computing cloud platform.

The new chip, called "Xiaohong," is the biggest built by China to date and is designed to improve systems that manage the behavior and interaction of quantum bits, or qubits, in quantum computers, state-owned China Daily reported. The scientists hope the chip will help to scale up existing quantum computers so they can handle more complex tasks.

Xiaohong was developed by scientists at the Center for Excellence in Quantum Information and Quantum Physics, part of the Chinese Academy of Sciences (CAS). Chinese quantum computing company QuantumCTek, which received the first Xiaohong chip, will now reportedly work alongside China Telecom Quantum Group to integrate the 504-qubit chip into a new quantum computer.

This system will then be made available to researchers worldwide via a quantum computing cloud platform developed by China Telecom Quantum Group, according to the report.

Wang Zhen, deputy general manager of China Telecom Quantum Group, said in a statement the new system would "allow users in various fields to conduct research on problems and algorithms of practical value efficiently, and accelerate the application of quantum computing in actual scenarios."

 Related: 'World's purest silicon' could lead to 1st million-qubit quantum computing chips

Xiaohong is designed to meet the performance standards of cloud-enabled quantum computing platforms like those made by IBM or AWS. But it's not intended as a technical rival to cutting-edge U.S. technology — such as the 1,121-qubit IBM Quantum Condor chip, said Gong Ming, a researcher at the Center for Excellence in Quantum Information and Quantum Physics.

Instead, the scientists hope access to Xiaohong via the cloud will promote the development of large-scale quantum computing measurement and control systems (QCMCSs).

Quantum computers work fundamentally differently from classical computers. Unlike classical bits, which can only be represented as 0 or 1, qubits can exist in multiple states simultaneously. This enables quantum computers to perform calculations in parallel and at near-unimaginable speeds if qubits are stitched together through quantum entanglement.

QCMCSs, meanwhile, are components that play a crucial role in quantum computing — acting as a bridge that connects traditional computers with quantum computers. This connection enables quantum computers to interpret commands received from classical computing environments and manage the state of qubits accordingly.

QuantumCTek will use Xiaohong to test the "kilo-qubit" quantum computing measurement and control systems developed in-house. This would "greatly influence the overall performance of quantum computers," Liang Futian, an associate professor at the Center for Excellence in Quantum Information and Quantum Physics, said in the statement.

While the 504-qubit Xiaohong chip is China’s largest quantum chip to date, it’s not the largest in the world. That title currently belongs to Atom Computing, which announced its behemoth 1,125-qubit quantum computer in October 2023. 

Previous notable contributions from China include the Jiuzhang 2.0 and Zuchongzhi 2.1 supercomputers. When China launched its Jiuzhang quantum computer in 2020, it claimed it was the world's fastest — reportedly surpassing Google's Sycamore supercomputer by 10 billion times.

Scientists have created an enhanced, ultra-pure form of silicon that could one day be the foundation for highly reliable "silicon-spin qubits" in powerful quantum computers.

While the bits in classical computers encode data as either 1 or 0, qubits in quantum computers can be a superposition of these two states — meaning they can achieve a quantum state known as "coherence" and occupy both 1 and 0 in parallel while processing calculations. 

These machines could potentially be more powerful than the world's fastest supercomputers but would need around a million qubits to achieve this, the scientists said. The largest quantum computer today has roughly 1,000 qubits. 

But a key challenge with quantum computing is that qubits are "noisy," meaning they are highly prone to interference, such as temperature changes, and need to be cooled to near absolute zero. Otherwise, they easily lose information and fail midway through operations. 

This means that even if we had a quantum computer with millions of qubits, many of those would be redundant even with error-correction technologies, making the machine extremely inefficient.

Tapping into silicon quantum computing

Qubits are normally made from superconducting metals such as tantalum and niobium because they possess near-infinite conductivity and near-infinite resistance.

But in a new study, published May 7 in the journal Nature Communications Materials, researchers proposed using a new, pure form of silicon — the semiconductor material used in conventional computers — as the basis for a qubit that is far more scalable than existing technologies.

Related: Quantum computing breakthrough could happen with just hundreds, not millions, of qubits using new error-correction system

Building qubits from semiconducting materials like silicon, gallium or germanium has advantages over superconducting metal qubits, according to the quantum computing company QuEra. The coherence times are relatively long, they are cheap to make, they operate at higher temperatures and they are extremely tiny — meaning a single chip can hold huge numbers of qubits. But impurities in semiconducting materials cause decoherence during computations, which makes them unreliable.

In the new study, the scientists proposed making a qubit out of silicon-28 (Si-28), which they described as the "world's purest silicon," after stripping away the impurities found in natural silicon. These silicon-based qubits would be less prone to failure, they said, and could be fabricated to the size of a pinhead.

Natural silicon is normally made up of three isotopes, or atoms of different masses — Si-28, Si-29 and Si-30. Natural silicon works well in conventional computing due to its metalloid properties, but problems arise when using it in quantum computing. 

Si-29 in particular, which makes up 5% of natural silicon, causes a "nuclear flip-flopping effect" that leads to decoherence and the loss of information. In the study, the scientists got around this by developing a new method to engineer silicon without Si-29 and Si-30 atoms.

Cheaper, more scalable quantum computing

"What we've been able to do is effectively create a critical 'brick' needed to construct a silicon-based quantum computer," lead study author Richard Curry, professor of advanced electronic materials at the University of Manchester, said in a statement. "It’s a crucial step to making a technology that has the potential to be transformative for humankind feasible."

Components for silicon-based quantum computers could in theory be built using the same methods used to manufacture classical electronic chips, which can fit billions of transistors onto a tiny circuit board, the scientists said. Silicon qubits, or silicon-spin qubits, are nothing new, but the quality of the silicon has never been as pure, they added, which is determined based on microscopy testing.

Silicon-based qubits could also be manufactured far more easily than other kinds of qubit because of existing chip fabrication methods. And, therefore, quantum computers that use them can be scaled to the million-qubit region much more quickly than competing methods, the researchers said.

"Now that we can produce extremely pure silicon-28, our next step will be to demonstrate that we can sustain quantum coherence for many qubits simultaneously," project co-supervisor David Jamieson, professor of physics at the University of Melbourne, said in the statement. "A reliable quantum computer with just 30 qubits would exceed the power of today's supercomputers for some applications."

Quantum bits, or qubits, made from electrons floating on top of liquid helium could one day power the next generation of quantum computers, according to a new study.

While the bits that power classical computers encode data as either 0 or 1, qubits can be a superposition of these two states — meaning they can occupy both in parallel while processing calculations. Computers built this way can one day be much more powerful than today’s fastest supercomputers — and promise to be transformative in several fields including drug discovery and tackling climate change.

Qubits are normally made by manipulating the spin state of an electron between its spin-up and spin-down positions, which represent 1 and 0. 

Other particles used as qubits include trapped ions, photons, artificial or real atoms and quasiparticles, according to Microsoft, and most qubits achieve a superposition by cooling a superconducting metal (which contains the particle) to absolute zero.

But in a study published Nov. 9 in the journal Physical Review Applied, scientists argue this conventional approach to building a qubit is challenging. That's because combining electrons and solid-state crystals (including metals) creates impurities in the material. This means qubits aren't uniform and, in turn, this leads to a higher chance of qubits failing during calculations.

Related: How could this new type of room-temperature qubit usher in the next phase of quantum computing?

These defects can cause several issues, including  “unpredictable electrical potential” and difficulty producing “many uniform qubits,” the scientists said in a statement. It also means that scaling up the number of qubits in a quantum system will amplify the error rate.

This led the scientists to propose a blueprint for a new type of qubit that is theoretically free from such errors. They believe that making electrons float in a vacuum above a pool of liquid helium would not introduce any defects into the system. This means far fewer qubits would be needed in a future quantum computer to achieve quantum supremacy — where the power of a quantum computer surpasses a classical computer — because you don't need to account for a high qubit failure rate.

“Solid-state crystals will always have some defects, which means we cannot create a perfect environment for electrons,” said lead author of the paper, Erika Kawakami, a physicist working at the RIKEN Center for Quantum Computing in Japan, in the statement. “That is problematic if we want to create a lot of uniform qubits. And so it's better to have qubits in [a] vacuum."

Building on past research

This approach to qubits isn’t new. In 1999, scientists proposed a physical system in which floating electrons formed qubits in a vacuum not far from the surface of liquid helium.

But because quantum computing research was only in its early stages, this research spanned just quantum gates — an essential but basic component of quantum mathematical operations that is made up of a small collection of qubits. Quantum gates are the foundation of quantum circuits and are predominantly used for creating quantum algorithms.

Over the last few years, quantum computing research has advanced greatly, leading Kawakami and her colleagues to expand on this previous research with a new theory in which a hybrid qubit is formed from two distinct states of floating electrons. The "charge state" uses an electric field to easy manipulate the electron over moderate distances with an electric field, while the "spin state" can be used to store data stably. Data is transferred between these two properties thanks to the interaction between these two states.

They’ve proposed a system that traps electrons on top of liquid helium using myriad small ferromagnetic pillars, allowing over 10 million qubits to fit into a postage stamp-sized area. In the next stage of this research, the scientists hope to test their theories by conducting a practical experiment with a prototype.

"We've proposed how to realize one-qubit and two-qubit gates using electrons on helium and estimated their fidelities," Kawakami added. "We've also specified how we can scale up the number of qubits. That is something new."

We're now one step closer to a "quantum internet" — an interconnected web of quantum computers — after scientists built a network of "quantum memories" at room temperature for the first time.

In their experiments, the scientists stored and retrieved two photonic qubits — qubits made from photons (or light particles) — at the quantum level, according to their paper published on Jan. 15 in the Nature journal, Quantum Information. 

The breakthrough is significant because quantum memory is a foundational technology that will be a precursor to a quantum internet – the next generation of the World Wide Web. 

Quantum memory is the quantum version of binary computing memory. While data in classical computing is encoded in binary states of 1 or 0, quantum memory stores data as a quantum bit, or qubit, which can also be a superposition of 1 and 0. If observed, the superposition collapses and the qubit is as useful as a conventional bit. 

Quantum computers with millions of qubits are expected to be vastly more powerful than today's fastest supercomputers — because entangled qubits (intrinsically linked over space and time) can make many more calculations simultaneously. 

Related: How could this new type of room-temperature qubit usher in the next phase of quantum computing?

As the name implies, the quantum internet is an internet infrastructure that relies on the laws of quantum mechanics to transmit data between quantum computers. But we need quantum memory for a quantum network to function. Because qubits adopt a superposition of 1 and 0, rather than either binary state as in classical computing, they can store and transmit more information with far greater density than conventional networks.

“To get these fleets of quantum memories to work together at a quantum level, and in a room temperature state, is something that is essential for any quantum internet on any scale. To our knowledge, this feat has not been demonstrated before, and we expect to build on this research,” said lead author Eden Figueroa, professor of physics and astronomy at Stony Brook University, in a statement. 

Building a network for quantum computing

Quantum networks built in recent years have needed to be cooled to absolute zero to operate, which limits their usefulness. But scientists from Stony Brook University developed a method to store two separate photons and – most importantly – successfully retrieve their quantum signature. They achieved this at room temperature by storing photons in a rubidium gas. 

This makes it more viable than previous experiments in designing and deploying a quantum internet in the future. However, they could only store the photons in this experiment for a fraction of a second, while storing qubits at cryogenic temperatures normally means they can last for more than an hour.

“The actual selling point of this was that they were able to take two independently stored photons, retrieve them at the same time, and interfere them,” Daniel Oi, a professor in quantum physics at the University of Strathclyde, told Live Science. “You get what’s called a HOM dip, or a Hong-Ou-Mandel dip, which is a characteristic quantum signature indicating that these two photons were identical.”

As well as being faster, quantum communications are inherently secure — while classical communications can be intercepted or manipulated. This is because any attempts to intercept and read information transmitted across the quantum network equates to observation — which would collapse the superposition of the qubits moving through the circuit.

This is an active field of research and a race is underway to develop the technologies that will help us build a quantum internet. In 2022, researchers in Switzerland stored a single photon using a similar method. That same year, China transmitted signals using quantum entanglement between two memory devices located 12.5 kilometers apart. 

The next stage is to develop a method for detecting when a quantum signal is ready to be retrieved, without destroying the properties of the signal through direct observation. Achieving this would pave the way for quantum repeaters, which are devices that can extend the range of a quantum signal. This would be a key precursor to a large-scale quantum internet.

“One of the holy grails of quantum memories is ‘How do you detect that you’ve actually stored a photon, without destroying the quantum properties of that photon, and do it in a way that is efficient and reliable?’,” said Oi.

By firing a Fibonacci laser pulse at atoms inside a quantum computer, physicists have created a completely new, strange phase of matter that behaves as if it has two dimensions of time. 

The new phase of matter, created by using lasers to rhythmically jiggle a strand of 10 ytterbium ions, enables scientists to store information in a far more error-protected way, thereby opening the path to quantum computers that can hold on to data for a long time without becoming garbled. The researchers outlined their findings in a paper published July 20 in the journal Nature. 

The inclusion of a theoretical "extra" time dimension "is a completely different way of thinking about phases of matter," lead author Philipp Dumitrescu, a researcher at the Flatiron Institute's Center for Computational Quantum Physics in New York City, said in a statement. "I've been working on these theory ideas for over five years, and seeing them come actually to be realized in experiments is exciting."

Related: Otherworldly 'time crystal' made inside Google quantum computer could change physics forever

The physicists didn't set out to create a phase with a theoretical extra time dimension, nor were they looking for a method to enable better quantum data storage. Instead, they were interested in creating a new phase of matter — a new form in which matter can exist, beyond the standard solid, liquid, gas, plasma. 

They set about building the new phase in the quantum computer company Quantinuum's H1 quantum processor, which consists of 10 ytterbium ions in a vacuum chamber that are precisely controlled by lasers in a device known as an ion trap. 

Ordinary computers use bits, or 0s and 1s, to form the basis of all calculations. Quantum computers are designed to use qubits, which can also exist in a state of 0 or 1. But that's just about where the similarities end. Thanks to the bizarre laws of the quantum world, qubits can exist in a combination, or superposition, of both the 0 and 1 states until the moment they are measured, upon which they randomly collapse into either a 0 or a 1. 

This strange behavior is the key to the power of quantum computing, as it allows qubits to link together through quantum entanglement, a process that Albert Einstein dubbed "spooky action at a distance." Entanglement couples two or more qubits to each other, connecting their properties so that any change in one particle will cause a change in the other, even if they are separated by vast distances. This gives quantum computers the ability to perform multiple calculations simultaneously, exponentially boosting their processing power over that of classical devices.

But the development of quantum computers is held back by a big flaw: Qubits don't just interact and get entangled with each other; because they cannot be perfectly isolated from the environment outside the quantum computer, they also interact with the outside environment, thus causing them to lose their quantum properties, and the information they carry, in a process called decoherence.

"Even if you keep all the atoms under tight control, they can lose their 'quantumness' by talking to their environment, heating up or interacting with things in ways you didn't plan," Dumitrescu said.

To get around these pesky decoherence effects and create a new, stable phase, the physicists looked to a special set of phases called topological phases. Quantum entanglement doesn't just enable quantum devices to encode information across the singular, static positions of qubits, but also to weave them into the dynamic motions and interactions of the entire material — in the very shape, or topology, of the material's entangled states. This creates a "topological" qubit that encodes information in the shape formed by multiple parts rather than one part alone, making the phase much less likely to lose its information.

A key hallmark of moving from one phase to another is the breaking of physical symmetries — the idea that the laws of physics are the same for an object at any point in time or space. As a liquid, the molecules in water follow the same physical laws at every point in space and in every direction. But if you cool water enough so that it transforms into ice, its molecules will pick regular points along a crystal structure, or lattice, to arrange themselves across. Suddenly, the water molecules have preferred points in space to occupy, and they leave the other points empty; the spatial symmetry of the water has been spontaneously broken.

Creating a new topological phase inside a quantum computer also relies on symmetry breaking, but with this new phase, the symmetry is not being broken across space, but time. 

Related: World's 1st multinode quantum network is a breakthrough for the quantum internet

By giving each ion in the chain a periodic jolt with the lasers, the physicists wanted to break the continuous time symmetry of the ions at rest and impose their own time symmetry — where the qubits remain the same across certain intervals in time — that would create a rhythmic topological phase across the material.

But the experiment failed. Instead of inducing a topological phase that was immune to decoherence effects, the regular laser pulses amplified the noise from outside the system, destroying it less than 1.5 seconds after it was switched on. 

After reconsidering the experiment, the researchers realized that to create a more robust topological phase, they would need to knot more than one time symmetry into the ion strand to decrease the odds of the system getting scrambled. To do this, they settled on finding a pulse pattern that did not repeat simply and regularly but nonetheless showed some kind of higher symmetry across time. 

This led them to the Fibonacci sequence, in which the next number of the sequence is created by adding the previous two. Whereas a simple periodic laser pulse might just alternate between two laser sources (A, B, A, B, A, B, and so on), their new pulse train instead ran by combining the two pulses that came before (A, AB, ABA, ABAAB, ABAABABA, etc.).

This Fibonacci pulsing created a time symmetry that, just like a quasicrystal in space, was ordered without ever repeating. And just like a quasicrystal, the Fibonacci pulses also squish a higher dimensional pattern onto a lower dimensional surface. In the case of a spatial quasicrystal such as Penrose tiling, a slice of a five-dimensional lattice is projected onto a two-dimensional surface. When looking at the Fibonacci pulse pattern, we see two theoretical time symmetries get flattened into a single physical one.

"The system essentially gets a bonus symmetry from a nonexistent extra time dimension," the researchers wrote in the statement. The system appears as a material that exists in some higher dimension with two dimensions of time — even if this may be physically impossible in reality. 

When the team tested it, the new quasiperiodic Fibonacci pulse created a topographic phase that protected the system from data loss across the entire 5.5 seconds of the test. Indeed, they had created a phase that was immune to decoherence for much longer than others.

"With this quasi-periodic sequence, there's a complicated evolution that cancels out all the errors that live on the edge," Dumitrescu said. "Because of that, the edge stays quantum-mechanically coherent much, much longer than you'd expect."

Although the physicists achieved their aim, one hurdle remains to making their phase a useful tool for quantum programmers: integrating it with the computational side of quantum computing so that it can be input with calculations.

"We have this direct, tantalizing application, but we need to find a way to hook it into the calculations," Dumitrescu said. "That's an open problem we're working on."

Originally published on Live Science.

Quantum computers are often touted as the next generation of computing. They rely on the laws of quantum mechanics — the weird behavior of particles at a subatomic scale — to process information. Currently, quantum computers are too small, too difficult to maintain and too error-prone to compete with today's best classical computers. However, many experts expect quantum computing to one day overtake classical computing for specific tasks.

The technologies that enable quantum computing have advanced rapidly in the past few years. One day, they may be able to solve problems that are too complex for even today's most powerful conventional computers. This massive performance gain could open the door to many exciting uses including in pharmaceuticals, climate modeling and manufacturing, all of which rely on hugely complex simulations.

Everything you need to know about quantum computers

Glossary

• Qubits: The basic unit of information in a quantum computer. Unlike a classical computer bit, a qubit can be both 0 and 1 at the same time, which allows quantum computers to process more information at once.
• Superposition: A special property governed by quantum mechanics that allows qubits to exist in multiple states (0 and 1) at the same time.
• Quantum entanglement: A phenomenon where the fates of two particles are linked, even if they are physically separated. Qubits can be entangled so that the state of one instantly affects the state of others.
• Quantum algorithms: Step-by-step procedures that are designed to run on quantum computers to perform calculations. These algorithms leverage superposition and quantum entanglement to allow for more complex operations.
• Quantum supremacy: The point at which quantum computers can reliably outperform the most powerful classical supercomputers.

Quantum computing pictures

Discover more

• Quantum mechanics: Definitions, axioms, and key concepts of quantum physics
• Will we ever have quantum laptops?
• Do quantum universes really exist?

Scientists have gotten one step closer to a quantum internet by creating the world's first multinode quantum network. 

Researchers at the QuTech research center in the Netherlands created the system, which is made up of three quantum nodes entangled by the spooky laws of quantum mechanics that govern subatomic particles. It is the first time that more than two quantum bits, or "qubits," that do the calculations in quantum computing have been linked together as "nodes," or network endpoints. 

Researchers expect the first quantum networks to unlock a wealth of computing applications that can't be performed by existing classical devices — such as faster computation and improved cryptography.

Related: 12 stunning quantum physics experiments 

"It will allow us to connect quantum computers for more computing power, create unhackable networks and connect atomic clocks and telescopes together with unprecedented levels of coordination," Matteo Pompili, a member of the QuTech research team that created the network at Delft University of Technology in the Netherlands, told Live Science. "There are also loads of applications that we can't really foresee. One could be to create an algorithm that will run elections in a secure way, for instance." 

In much the same way that the traditional computer bit is the basic unit of digital information, the qubit is the basic unit of quantum information. Like the bit, the qubit can be either a 1 or a 0, which represent two possible positions in a two-state system. 

But that's just about where the similarities end. Thanks to the bizarre laws of the quantum world, the qubit can exist in a superposition of both the 1 and 0 states until the moment it is measured, when it will randomly collapse into either a 1 or a 0. This strange behavior is the key to the power of quantum computing, as it allows a qubit to perform multiple calculations simultaneously. 

Related: The 18 biggest unsolved mysteries in physics

The biggest challenge in linking those qubits together into a quantum network is in establishing and maintaining a process called entanglement, or what Albert Einstein dubbed "spooky action at a distance." This is when two qubits become coupled, linking their properties so that any change in one particle will cause a change in the other, even if they are separated by vast distances. 

You can entangle quantum nodes in a lot of ways, but one common method works by first entangling the stationary qubits (which form the network's nodes) with photons, or light particles, before firing the photons at each other. When they meet, the two photons also become entangled, thereby entangling the qubits. This binds the two stationary nodes that are separated by a distance. Any change made to one is reflected by an instantaneous change to the other. 

"Spooky action at a distance" lets scientists change the state of a particle by altering the state of its distant entangled partner, effectively teleporting information across big gaps. But maintaining a state of entanglement is a tough task, especially as the entangled system is always at risk of interacting with the outside world and being destroyed by a process called decoherence. 

This means, first, that the quantum nodes have to be kept at extremely cold temperatures inside devices called cryostats to minimize the chances that the qubits will interfere with something outside the system. Second, the photons used in the entanglement can't travel very long distances before they are absorbed or scattered, — destroying the signal being sent between two nodes.

"The problem is, unlike classical networks, you cannot amplify quantum signals. If you try to copy the qubit, you destroy the original copy," Pompili said, referring to physics' "no-cloning theorem," which states that it is impossible to create an identical copy of an unknown quantum state. "This really limits the distances we can send quantum signals to the tens of hundreds of kilometers. If you want to set up quantum communication with someone on the other side of the world, you'll need relay nodes in between."

To solve the problem, the team created a network with three nodes, in which photons essentially "pass" the entanglement from a qubit at one of the outer nodes to one at the middle node. The middle node has two qubits — one to acquire an entangled state and one to store it. Once the entanglement between one outer node and the middle node is stored, the middle node entangles the other outer node with its spare qubit. With all of this done, the middle node entangles its two qubits, causing the qubits of the outer nodes to become entangled.

But designing this weird quantum mechanical spin on the classic "river crossing puzzle" was the least of the researchers' troubles — weird, for sure, but not too tricky an idea. To make the entangled photons and beam them to the nodes in the right way, the researchers had to use a complex system of mirrors and laser light. The really tough part was the technological challenge of reducing pesky noise in the system, as well as making sure all of the lasers used to produce the photons were perfectly synchronized.

"We're talking about having three to four lasers for every node, so you start to have 10 lasers and three cryostats that all need to work at the same time, along with all of the electronics and the synchronization," Pompili said. 

The three-node system is particularly useful as the memory qubit allows researchers to establish entanglement across the network node by node, rather than the more demanding requirement of doing it all at once. As soon as this is done, information can be beamed across the network.

Some of the researchers' next steps with their new network will be to attempt this information beaming, along with improving essential components of the network's computing abilities so that they can work like regular computer networks do. All of these things will set the scale that the new quantum network could reach.

They also want to see if their system will allow them to establish entanglement between Delft and The Hague, two Dutch cities that are roughly 6 miles (10 kilometers) apart.

"Right now, all of our nodes are within 10 to 20 meters [32 to 66 feet] of each other," Pompili said. "If you want something useful, you need to go to kilometers. This is going to be the first time that we're going to make a link between long distances."

The researchers published their findings April 16 in the journal Science.

Originally published on Live Science.