Category: Quantum

Scientists from the University of Chicago Pritzker School of Molecular Engineering (UChicago PME), Harvard, Stony Brook University, and Quantinuum created and tested a full set of operations based on non-Abelian anyons. Their results provide the first experimental demonstration that this approach can support the broad range of operations required for universal quantum computing.

“We demonstrated a so-called universal gate set — meaning that if you store information in these emergent versions of quarks, and you move them around, you can do any quantum computation you might want to do,” said Ruben Verresen, assistant professor of molecular engineering at UChicago PME and a co-author of the new study published in Nature.

A Possible Shortcut Around Costly Quantum Error Correction

The strategy could do more than help create a general-purpose quantum computer. It may also offer a more efficient route toward reliable quantum machines.

Quantum computers are extremely vulnerable to errors, so researchers typically protect information by distributing it across many physical qubits. These error correction methods can preserve data, but they usually do not provide every operation needed to perform universal quantum computation on that protected information.

To fill the gap, engineers often rely on specially prepared resources called “magic states.” Producing them usually requires an intensive purification process known as distillation, which can consume a large fraction of a quantum computer’s available qubits. The new results suggest that non-Abelian anyons may offer a way around that expensive step.

“Non-Abelian codes are a dark horse in the race to quantum error correction,” said Henrik Dreyer, managing director and scientific lead at Quantinuum’s Munich office and a co-author of the study. “In this work we show the first universal gate set in a non-Abelian code, which demonstrates that fault-tolerant computations can in principle be done without resorting to magic state distillation or cultivation, which are the most expensive operations in standard quantum error correction codes.”

Why Non-Abelian Anyons Are Different

Ordinary qubits encode information using two basic states, along with quantum combinations of those states. Non-Abelian anyons work in a fundamentally different way.

These anyons do not appear as ordinary standalone particles in nature. Instead, scientists create them inside quantum circuits by entangling many conventional qubits into a collective state that behaves as though it were a new type of particle with its own unusual rules.

“The way I think about these codes is they’re creating little universes — alternative universes, but ones that reflect some of the properties of our own,” Verresen said.

Each non-Abelian anyon carries an internal state that changes when one anyon is moved around another in a process known as braiding. The sequence of those braiding operations matters (that’s what non-Abelian means), allowing information to be encoded and manipulated in ways that are unavailable to ordinary particles.

Because the information is spread across many entangled qubits rather than stored in a single location, it can also be naturally protected from some of the small disturbances that frequently disrupt conventional qubits. Braiding the anyons can also perform computational operations.

Why Braiding Alone Was Not Enough

In 2024, a research team that included Verresen used a Quantinuum trapped-ion computer to create anyons associated with a symmetry group called D4 — the rotations and reflections that leave a square unchanged — demonstrating this form of non-Abelian order on quantum hardware for the first time.

That experiment showed that the unusual particles could be created and manipulated, but braiding them alone was not sufficient to carry out every operation required for universal quantum computing.

“In that work, we didn’t demonstrate that those emergent forces were enough to do quantum computation,” said Verresen. “That particular universe we created was not powerful enough.”

Fusion Unlocks Universal Quantum Operations

For the new study, the researchers switched to another symmetry known as S3 — the rotations and mirror-image flips that leave an equilateral triangle unchanged — and created the corresponding anyons on Quantinuum’s H2 trapped-ion processor using 54 entangled qubits.

The S3 system had the properties needed for universal quantum computation, but only when braiding was combined with another operation called fusion. During fusion, two anyons are brought together and the resulting state is measured.

The underlying concept was proposed theoretically in 2003 by Carlos Mochon, then a student of John Preskill at Caltech. Turning that theoretical idea into an experiment that could actually run on quantum hardware, however, required extensive additional theoretical and experimental work.

The researchers used pairs of anyons to encode “topological qutrits” which store three possible levels of quantum information instead of the two levels used by ordinary qubits.

By combining different braiding and fusion operations, the team demonstrated three key tools: one entangling gate produced through braiding and two different measurements created through fusion. Together, those operations can in principle produce any quantum operation, including operations that braiding alone could not achieve.

Beyond their possible computing applications, these unusual quantum states could also help researchers investigate fundamental aspects of physics.

“It is gratifying to see ideas we have spent our PhD work thinking about realized in the lab, and it has been made possible by remarkable advances in quantum hardware over the past few years,” said Anasuya Lyons and Chiu Fan Bowen Lo, graduate students at Harvard University in the group of Ashvin Vishwanath who helped lead the work.

Toward Fault-Tolerant Quantum Computers

The researchers also demonstrated that non-Abelian anyons could directly produce a magic state using topological operations — sidestepping the costly distillation process used in most quantum systems.

The experiment did not yet include active error correction. Instead, the researchers focused on testing the individual building blocks of the method and confirming that they could create a magic state consistent with theoretical expectations.

“So far, we’ve ignored the question of error correction. Here, it’s more like a proof of principle,” Verresen said.

The next major step will be combining these operations with active error correction. If that can be achieved, non-Abelian anyons could eventually become a practical basis for large-scale, fault-tolerant quantum computers. Verresen is already working with other PME researchers on new techniques for stabilizing non-Abelian quantum memories.

Published in Physical Review X, the experiment provides the first demonstration of a prediction proposed more than 20 years ago. The approach could eventually offer a new foundation for practical quantum technologies.

Connecting Distant Quantum Bits

Entanglement is one of the defining phenomena of quantum physics. It allows particles or systems to share correlations that cannot be explained by classical physics. Creating distributed entanglement between physically separated qubits (quantum bits) could be important for building larger quantum computers and future quantum networks.

Previous attempts to entangle distant qubits have generally followed one of two strategies. One method sends a single, actively controlled photon from one qubit to another. Another has each qubit emit a photon, with the two photons then matched in an effort to generate entanglement.

The second approach was recognized by the 2022 Nobel Prize in Physics. However, it still depends on repeated measurements and post-selection, and even then, the process does not always successfully produce entanglement.

PhD student Alejandro Andrés-Juanes and professor Johannes Fink at the Institute of Science and Technology Austria (ISTA) worked with international collaborators to develop a different solution. Their system uses a quantum bath that automatically brings distant qubits into synchronization. In a prototype device, the researchers used a shared source of correlated light particles to entangle two separated qubits, experimentally realizing an idea that had remained theoretical for more than two decades.

Fully Autonomous Quantum Entanglement

Quantum entanglement can take several forms. Continuous-variable entangled states can be produced efficiently and are therefore relatively accessible. They can be compared with a pendulum, whose position and momentum change continuously.

Many useful quantum technologies, however, depend on “discrete-variable” systems. These involve ‘all-or-nothing’ forms of entanglement that stationary qubits can use. The challenge for the ISTA team was finding a way to connect these readily available continuous forms of entanglement with the discrete forms needed for practical applications.

“In this work, we aimed to overcome this mismatch between the readily available and the practically useful forms of entanglement,” says Andrés-Juanes. “By stabilizing the entangled states remotely, our approach is fully autonomous and requires no active control or measurement.”

A Quantum Bath Powered by Correlated Light

Maintaining both entanglement and quantum coherence is one of the major difficulties facing quantum computing.

The researchers addressed that problem by making the qubits’ surrounding environment itself responsible for producing and stabilizing entanglement.

“In our method, the quantum bath — meaning the qubits’ environment — is the source of entanglement. It creates a new ground state through a continuous stream of correlated photons,” says Fink. “This way, the entangled qubit state is stabilized, even beyond the qubits’ own ‘lifetime’, and remains always available as a resource for further quantum processing. This makes the approach conceptually significant.”

Because the entangled state remains available, researchers can access it whenever it is needed. That differs from temporary entanglement, which has to be used during the brief period when it exists.

Microwave Photons Keep Qubits Connected

To couple the qubits with the entangled photon source, the researchers relied on microwave photons. These low-energy light particles are especially useful for manipulating quantum information and are already central to leading superconducting-qubit technology.

Optical photons serve a different role and are commonly used in optics and atomic physics. They may also become important for carrying quantum information between distant quantum computers through fiber optics, an area that the Fink group at ISTA is also investigating.

Measuring the Hidden Quantum State

The researchers needed to confirm that the two qubits were actually synchronized inside the quantum bath. To do that, they used quantum tomography, a technique that reconstructs a quantum system by examining many different ‘slices’ of its behavior.

“Qubits can be in a superposition of states, but all these states collapse when we measure them, leaving us with a 0 or 1 state,” says Andrés-Juanes.

Quantum tomography allowed the researchers to perform measurements lasting only 20-80 nanoseconds and use those observations to investigate the qubits’ underlying states. A nanosecond is one billionth of a second.

A 20-Year Prediction Becomes an Experiment

By successfully entangling two isolated qubits through a quantum bath, the ISTA researchers created a proof-of-concept laboratory prototype for the long-standing theoretical proposal.

“We present a relatively simple method that could be scaled up to synchronize multiple distant qubits,” says Andrés-Juanes.

The new approach is promising, but it is not yet as efficient as methods that actively control qubit states. “Our method currently transfers about 10% of the bath’s available entanglement.”

The researchers suggest that one reason the idea took more than two decades to demonstrate is that the original theory was developed under idealized conditions that are difficult to reproduce experimentally.

“Our experiments helped us reveal several factors that may have prevented scientists from designing a functional quantum bath using a single source of correlated photons for distributed entanglement,” says Fink.

The prototype developed at ISTA could provide new opportunities for quantum-optics experiments. It may also contribute to efforts to expand quantum processors and ultimately move them closer to fault-tolerant operation.

The quantum computation was completed in about 15 minutes, while leading classical computing methods would require an impractical amount of time to perform the same task.

IBM and researchers at the University of Chicago have announced a quantum computing demonstration that meets key criteria for quantum advantage. The experiment performed a calculation beyond the practical reach of leading classical simulation techniques while also providing evidence that the quantum computer produced a reliable result.

In a new paper, “Sampling hard circuits with verifiably high fidelity,” the researchers describe how they achieved both goals using a newly designed form of encoded quantum circuit. The work represents one of the largest demonstrations of logical quantum computing reported so far.

The circuits and experimental results have also been made publicly available through the Quantum Advantage Tracker.

Why Quantum Results Are So Difficult to Verify

Researchers have long used a benchmark called random circuit sampling (RCS) to explore whether quantum computers can outperform conventional machines.

In simplified terms, RCS challenges a quantum computer to generate patterns that become so complicated that a classical computer cannot efficiently reproduce them. That makes the benchmark useful for testing the limits of classical simulation.

But it also creates a major problem. Once the quantum calculation becomes too difficult for a classical computer to reproduce, verifying that the quantum machine actually produced the correct result becomes increasingly difficult as well. Eventually, checking the answer can itself become infeasible unless researchers make strong assumptions about how the quantum computer behaves internally.

The IBM and University of Chicago team approached this verification problem by developing a more structured alternative to RCS.

The researchers showed that their method preserves the same computational hardness criteria associated with RCS, meaning the problem remains extremely difficult for classical computers. At the same time, the added structure allows errors to be detected during the quantum computation.

“Verification remains one of the biggest challenges in firmly establishing experimental quantum advantage,” said Bill Fefferman, Associate Professor at the University of Chicago. “This experiment develops techniques to better characterize the fidelity of hard quantum states under noise, increasing confidence that the quantum computer is solving a computationally hard problem.”

Soumik Ghosh, PhD student in Fefferman’s group at the University of Chicago, added, “Beyond strengthening experimental validation, advances in verification have the potential to unlock practical applications for the next generation of quantum computers.”

70 Logical Qubits With Lower Error Rates

The experiment also included one of the world’s largest-known demonstrations of quantum error correction.

The researchers operated 70 logical qubits. Unlike individual physical qubits, logical qubits are encoded in a way that helps protect quantum information from errors and noise.

Using those logical qubits, the team carried out 2,415 logical two-qubit operations and 468 logical “T gates,” two measures that reflect the complexity of the quantum circuit.

The encoded design significantly improved reliability. Effective logical error rates were 10 times lower than the underlying physical error rates, allowing the circuit to maintain unusually high fidelity even while performing a large number of quantum operations.

IBM Says Quantum Advantage Has Entered a New Stage

“We are now firmly in the quantum advantage era,” said Jay Gambetta, Director of IBM Research and IBM Fellow. “We have demonstrated a quantum computation beyond the practical reach of classical computers that establishes, with statistical confidence, a lower bound on how faithfully it was executed. This milestone gives scientists, developers, and businesses a new foundation for trusting quantum computers as they scale to problems far beyond what we can achieve classically.”

The researchers found that many leading classical simulation methods would face prohibitive runtimes when attempting the same task.

The IBM quantum computer, by comparison, completed the computation in approximately 15 minutes.

A Step Toward Larger, More Trustworthy Quantum Computers

Speed alone is not enough for quantum computing to become useful at larger scales. Researchers also need ways to suppress errors and establish confidence that a quantum system produced a valid result.

This experiment advances both goals at once by combining large-scale logical quantum computing with a method for evaluating the reliability of a calculation that is already beyond practical classical simulation.

Error correction and trustworthy verification are considered essential for scaling quantum computers toward more difficult problems, making the new demonstration an important step toward that goal.

Scientists are getting a clearer picture of how thermodynamics behaves in the quantum world, and that progress could benefit both quantum technology and our understanding of familiar thermodynamic principles. Researchers at Aalto University have now taken an important step by demonstrating the first cyclic quantum heat engine built inside a superconducting circuit.

The experiment connects two areas of physics that normally describe very different scales. Quantum mechanics explains the behavior of matter at extremely small scales, even below the size of atoms, while thermodynamics describes how heat and energy behave in much larger systems, from collections of molecules to the universe itself. Bringing the two together raises a fundamental question: what happens to familiar thermodynamic processes when quantum effects such as tunneling, entanglement, and superposition enter the picture?

A Heat Engine Built for the Quantum World

Conventional heat engines turn heat into useful work. James Watt’s steam engine is one famous example, but the same basic concept remains central to modern transportation and electricity production, powering cars, ships, planes and many power plants.

The researchers have now created the world’s first superconducting quantum heat engine. The extremely small device combines a transmon qubit, a resonator and a quantum refrigerator.

Operating under ultracold quantum conditions, the engine was able to use the tiny amount of available heat to repeatedly produce positive work. Achieving this kind of cyclic operation has been an important objective for researchers working on quantum heat engines. The result provides a proof of concept for superconducting heat engines that could eventually contribute to improved quantum computing technology.

The study, led by Academy Professor Mikko Möttönen, was published in Nature Communications.

Recreating an Otto Cycle Near Absolute Zero

To make the engine operate, the researchers reproduced an Otto cycle inside a superconducting circuit. The Otto cycle is a thermodynamic process also used in car engines and other conventional machines.

“In our experiment, we built a nanofabricated heat engine using superconducting circuits and operated it in a cryostat near absolute zero. At its heart is a transmon qubit, one of the basic building blocks of modern quantum technologies,” says Tuomas Uusnäkki, the study’s first author.

The researchers connected the transmon qubit to a quantum circuit refrigerator, allowing them to control heat flow on the quantum scale and demonstrate that this heat could be transformed into measurable work. A conventional heat engine normally relies on separate hot and cold environments. In this system, however, the same quantum refrigerator can supply both heating and cooling.

“Our quantum-circuit refrigerator can be tuned to both heat and cool the qubit on demand. Using carefully timed control pulses, we drove the engine in an Otto cycle and monitored the qubit state as the engine ran,” explains Uusnäkki.

Measurements showed that heat passing through the qubit during the cycle was producing positive work.

“This is the first experimental demonstration of a cyclic quantum heat engine in superconducting circuits. Using a single controllable quantum refrigerator as both the hot and cold environment of the engine makes it simpler and more versatile,” says Uusnäkki.

Toward Autonomous Quantum Computer Hardware

The researchers are now trying to improve the design and eventually develop a fully autonomous heat engine. One possible use would be reading out qubits without having to carry a microwave pulse from millikelvin temperatures all the way to room temperature.

That capability could become especially valuable as quantum computers grow. Autonomous devices integrated directly into superconducting circuits could reduce both the cost and complexity of machines containing very large numbers of qubits.

“Finland’s Quantum Technology Strategy envisions a quantum computer with one thousand logical qubits by 2035, which probably means hundreds of thousands of physical qubits. Doing that with current technology requires millions of microwave cables costing thousand euros each. The cables also introduce noise into the system. Using autonomous devices instead would mostly eliminate the need for those cables,” Möttönen says.

Reducing the need for those microwave connections could therefore address two challenges at once: the enormous hardware requirements of large quantum computers and the unwanted noise that cables can introduce into quantum systems.

The pioneering experiment was carried out using OtaNano, Finland’s national research infrastructure for nano, micro and quantum technology. Funding came from the Research Council of Finland and the Finnish Cultural Foundation.

In a new perspective article published in Science, Dr. Marlan Scully of Texas A&M University reflects on the remarkable evolution of quantum mechanics, from an abstract theory about tiny particles to a powerful framework helping researchers tackle some of the most difficult questions in science.

“Quantum mechanics started as a way to explain the behavior of tiny particles,” said Scully, who is also affiliated with Princeton University. “Now it’s driving innovations that were unimaginable just a generation ago.”

Scully has played a major role in advancing the field. He co-authored the influential textbook Quantum Optics, a resource that has educated generations of physicists. His research in coherent nanoscale laser spectroscopy helped make it possible to study molecules with atomic-scale precision. He has also developed groundbreaking concepts involving quantum heat engines, which challenge traditional assumptions about thermodynamic efficiency and may one day lead to new energy technologies.

From Schrödinger’s Cat to Quantum Technology

One of the most famous illustrations of quantum mechanics came in 1935, when Erwin Schrödinger proposed his cat paradox. The thought experiment suggested that a cat could exist in a state that is both alive and dead until it is observed. Schrödinger introduced the idea to highlight how strange quantum theory appeared.

Today, those once-bizarre concepts have moved far beyond philosophical debates.

“That ‘quantum weirdness’ is no longer just a philosophical puzzle,” said Scully. “It’s the foundation of quantum computing, quantum cryptography and even gravitational wave detection.”

The foundations of quantum mechanics were built by pioneers including Schrödinger and Werner Heisenberg. They developed two different mathematical approaches, wave mechanics and matrix mechanics, to describe quantum systems. Over time, these approaches were unified and contributed to the development of quantum field theory, which explains how particles interact through electromagnetic and nuclear forces.

Their work expanded on Niels Bohr’s early atomic model, which pictured electrons orbiting the nucleus much like planets orbit the sun. While later discoveries refined that picture, Bohr’s model helped pave the way for modern quantum theory.

Quantum Coherence and the Rise of Lasers

Among the most important concepts in quantum mechanics is quantum coherence. This phenomenon allows particles such as atoms and photons to remain linked in a coordinated state, even across significant distances.

Quantum coherence led directly to the development of the laser, a technology that many once believed could never work. Today, lasers are used throughout modern society, from supermarket barcode scanners to vision correction procedures and advanced scientific instruments.

Coherence is also closely tied to quantum entanglement, the phenomenon that prompted Albert Einstein to describe it as “spooky action at a distance.”

Entanglement enables particles to share information through uniquely quantum properties. These effects form the basis of quantum encryption systems and improve the sensitivity of sophisticated instruments such as the Laser Interferometer Gravitational-Wave Observatory (LIGO), which detects tiny ripples in spacetime.

Quantum Heat Engines Challenge Classical Limits

One of the more surprising applications of quantum physics involves quantum heat engines.

Traditional engines are constrained by the Carnot Limit, which defines the maximum efficiency allowed by classical thermodynamics. Researchers have found that by exploiting quantum coherence, it may be possible to create engines that exceed those classical limits.

“It’s a striking example of how quantum principles can rewrite the rules of classical physics,” Scully said.

Quantum Biology, Gravity, and Turbulence

The influence of quantum mechanics now extends far beyond physics.

In biology, techniques such as coherent Raman spectroscopy allow researchers to examine viruses and other structures at the nanoscale, providing valuable insights into the microscopic world.

Quantum ideas are also shaping efforts to understand the universe itself. Scientists working on concepts such as string theory and quantum gravity are attempting to reconcile quantum mechanics with Einstein’s theory of relativity, one of the biggest unsolved problems in modern physics.

Researchers are even applying quantum concepts to the long-standing challenge of understanding turbulence. The chaotic motion of air and fluids affects weather patterns, climate systems, and aircraft performance. By studying superfluid helium, a substance that displays unusual quantum behavior, scientists are uncovering patterns that could improve climate modeling, storm prediction, and aviation safety.

The Next Century of Quantum Discovery

Despite a century of success, quantum mechanics continues to raise profound questions.

Can gravity be quantized (i.e., does gravity behave like other forces at the quantum level)? Could quantum computers transform medicine and materials science? What new insights about the universe might emerge from future quantum technologies?

Scully believes the search for answers is only beginning.

“At the start of the 20th century, many thought physics was complete,” he said. “Now, in the 21st century, we know the adventure is just beginning.”

Five Ways Quantum Mechanics Affects Everyday Life

1. Lasers From grocery store scanners to eye surgery, lasers depend on quantum principles that amplify light.
2. Secure communication Quantum cryptography can create highly secure codes that help protect sensitive information.
3. Faster computing Quantum computers have the potential to solve certain problems in seconds that could take classical computers thousands of years.
4. Better measurements Gravitational wave observatories use quantum techniques such as “squeezed light” to detect tiny distortions in spacetime and reveal new details about the universe.
5. Medical breakthroughs Quantum imaging methods help scientists study viruses, molecules, and other biological structures at the atomic scale.

Researchers can arrange these layers into even more complicated structures, including quasicrystals and super-moiré materials. But predicting how these exotic materials will behave is extraordinarily difficult. Quasicrystals are so mathematically complex that simulating them can involve more than a quadrillion numbers, a scale far beyond the reach of today’s most powerful supercomputers.

Quantum Algorithm Solves Massive Materials Problem

Scientists at Aalto University’s Department of Applied Physics have now developed a quantum-inspired algorithm capable of handling these enormous non-periodic quantum materials almost instantly. Assistant Professor Jose Lado says the work also highlights a promising feedback cycle within quantum technology itself.

“Crucially, these new quantum algorithms can enable the development of new quantum materials to build new paradigms of quantum computers, creating a productive two-way feedback loop between quantum materials and quantum computers,” he explains.

The advance could eventually support the development of dissipationless electronics, which conduct electricity without energy loss. Such systems may help reduce the growing heat and energy demands of AI-driven data centers.

The research team was led by Lado and included doctoral researcher Tiago Antão, who served as the paper’s main author; QDOC doctoral researcher Yitao Sun; and Academy Research Fellow Adolfo Fumega. Their findings were recently published in Physical Review Letters as an Editor’s Suggestion.

Simulating Topological Quasicrystals

The researchers focused on topological quasicrystals, unusual materials that host unconventional quantum excitations. These excitations are especially valuable because they help protect electrical conductivity from disruptive noise and interference. However, they are distributed unevenly throughout the already highly complex structure of a quasicrystal.

Rather than attempting to directly calculate the full structure of the material, the team reformulated the challenge using methods similar to those used by quantum computers.

“Quantum computers work in exponentially large computational spaces, so we used a special family of algorithms to encode those spaces, known as tensor networks, to compute a quasicrystal with over 268 million sites. Our algorithm shows how colossal problems in quantum materials can be directly solved with the exponential speed-up that comes from encoding the problem as a quantum many-body system,” Antão says.

At this stage, the work remains theoretical and was carried out through simulations, but researchers say experimental testing and future applications are already coming into view.

“The quantum-inspired algorithm we demonstrated enables us to create super-moiré quasicrystals several orders of magnitude above the capabilities of conventional methods. That is an instrumental step towards designing topological qubits with super-moiré materials for use in quantum computers, for example,” Lado says.

Toward Practical Quantum Computing Applications

According to Lado, the algorithm could eventually be adapted to operate on actual quantum computers once the hardware becomes sufficiently advanced.

“Our method can be adapted to run on real quantum computers, once they reach necessary scale and fidelity. In particular, the new AaltoQ20 and the Finnish Quantum Computing Infrastructure can play a significant role for future demonstrations,” Lado says.

The findings suggest that studying and designing exotic quantum materials may become one of the earliest practical applications for quantum algorithms and quantum computing systems.

The project also brings together two major areas of Finnish quantum research: quantum materials and quantum algorithms. It is part of Lado’s ERC Consolidator grant ULTRATWISTROICS, which focuses on designing topological qubits using van der Waals materials, as well as the Center of Excellence in Quantum Materials QMAT, whose goal is to advance the quantum technologies of the future.

The results, published in Science Advances, could enhance simulations of how liquids and gases behave, known as fluid dynamics. These types of models are essential in fields such as climate science, transportation, medicine, and energy production.

Why Quantum Computing Makes a Difference

The improved accuracy appears to come from how quantum computers process information. Unlike traditional computers that use bits set to either 1 or 0, quantum computers use qubits, which can exist as 1, 0, or anything in between. In addition, each qubit can influence others, allowing a relatively small number of qubits to represent an enormous number of possible states.

Professor Peter Coveney, senior author from UCL Chemistry and the Advanced Research Computing Centre, explained the challenge: “To make predictions about complex systems, we can either run a full simulation, which might take weeks — often too long to be useful — or we can use an AI model which is quicker but more unreliable over longer time scales.

“Our quantum-informed AI model means we could provide more accurate predictions quickly. Making predictions about fluid flow and turbulence is a fundamental science challenge but it also has many applications. Our method can be used in climate forecasting, in modeling blood flow and the interaction of molecules, or to better design wind farms so they generate more energy.”

How the Hybrid Quantum-AI Method Works

Although quantum computers are widely expected to surpass classical machines in power, their real-world use has so far been limited. This new approach integrates quantum computing into a specific stage of the AI training process.

Typically, AI models learn from large datasets generated by simulations or observations. In this case, the data is first processed by a quantum computer, which identifies key statistical patterns that remain stable over time. These patterns, known as invariant statistical properties, are then used to guide the training of an AI model running on a conventional supercomputer.

Higher Accuracy With Less Memory

The quantum-informed AI system delivered about 20 percent greater accuracy compared to standard AI models that did not use quantum-derived patterns. It also maintained stable predictions over longer periods, even when modeling chaotic systems.

Another major advantage was efficiency. The method required hundreds of times less memory, making it far more practical for large-scale simulations.

Quantum Effects Behind the Efficiency

This performance boost comes from two defining features of quantum computing. Entanglement allows qubits to influence each other regardless of distance, while superposition enables a qubit to exist in multiple states at once until measured. Together, these properties allow quantum systems to process vast amounts of information in a compact form.

Demonstrating Practical Quantum Advantage

First author Maida Wang of the UCL Centre for Computational Science said: “Our new method appears to demonstrate ‘quantum advantage’ in a practical way — that is, the quantum computer outperforms what is possible through classical computing alone. These findings could inspire the development of novel classical approaches that achieve even higher accuracy, though they would likely lack the remarkable data compression and parameter efficiency offered by our method. The next steps are to scale up the method using larger datasets and to apply it to real-world situations which typically involve even more complexity. In addition, a provable theoretical framework will be proposed.”

Co-first author Xiao Xue, from Advanced Research Computing at UCL, added: “In this work, we demonstrate for the first time that quantum computing can be meaningfully integrated with classical machine learning methods to tackle complex dynamical systems, including fluid mechanics. It is exciting to see this kind of ‘quantum-informed’ approach moving towards practical use.”

Capturing the Physics of Complex Systems

The researchers suggest that quantum computers are particularly well suited to modeling these systems because they can compactly represent their underlying physics. Many complex systems behave in ways that resemble quantum effects, where changes in one region can influence distant parts of the system, similar to entanglement.

Overcoming Limits of Current Quantum Hardware

Current quantum computers face challenges such as noise, errors, and interference, which often require large numbers of measurements. The new method avoids these issues by using the quantum computer only once during the workflow, rather than repeatedly exchanging data between quantum and classical systems.

Experiment Details and Future Potential

The study used a 20-qubit IQM quantum computer connected to powerful classical computing resources at the Leibniz Supercomputing Centre in Germany.

To function, quantum computers must operate at extremely low temperatures, around minus 273C (close to absolute zero, colder than anything in space).

The research was funded by UCL and the UK’s Engineering and Physical Sciences Research Council (EPSRC), with additional support from IQM Quantum Computers and the Leibniz Supercomputing Centre in Munich.

As researchers continue to scale up this approach, it could open the door to more accurate and efficient predictions across a wide range of scientific and engineering applications.

Qubits are the fundamental units of quantum computers, which scientists hope will one day outperform today’s most powerful machines. But qubits are extremely sensitive. The materials used to build them often contain tiny defects that scientists still do not fully understand. These microscopic imperfections can shift position hundreds of times per second. As they move, they alter how quickly a qubit loses energy and with it valuable quantum information.

Until recently, standard testing methods took up to a minute to measure qubit performance. That was far too slow to capture these rapid fluctuations. Instead, researchers could only determine an average energy loss rate, masking the true and often unstable behavior of the qubit.

It is somewhat like asking a strong workhorse to pull a plow while obstacles constantly appear in its path faster than anyone can react. The animal may be capable, but unpredictable disruptions make the job much harder.

FPGA Powered Real Time Qubit Control

A research team from the Niels Bohr Institute’s Center for Quantum Devices and the Novo Nordisk Foundation Quantum Computing Programme, led by postdoctoral researcher Dr. Fabrizio Berritta, developed a real time adaptive measurement system that tracks changes in the qubit energy loss (relaxation) rate as they occur. The project involved collaboration with scientists from the Norwegian University of Science and Technology, Leiden University, and Chalmers University.

The new approach relies on a fast classical controller that updates its estimate of a qubit’s relaxation rate within milliseconds. This matches the natural speed of the fluctuations themselves, rather than lagging seconds or minutes behind as older methods did.

To achieve this, the team used a Field Programmable Gate Array (FPGA), a type of classical processor designed for extremely rapid operations. By running the experiment directly on the FPGA, they could quickly generate a “best guess” of how fast the qubit was losing energy using only a few measurements. This eliminated the need for slower data transfers to a conventional computer.

Programming FPGAs for such specialized tasks can be challenging. Even so, the researchers succeeded in updating the controller’s internal Bayesian model after every single qubit measurement. That allowed the system to continually refine its understanding of the qubit’s condition in real time.

As a result, the controller now keeps pace with the qubit’s changing environment. Measurements and adjustments happen on nearly the same timescale as the fluctuations themselves, making the system roughly one hundred times faster than previously demonstrated.

The work also revealed something new. Scientists did not previously know just how quickly fluctuations occur in superconducting qubits. These experiments have now provided that insight.

Commercial Quantum Hardware Meets Advanced Control

FPGAs have long been used in other scientific and engineering fields. In this case, the researchers used a commercially available FPGA based controller from Quantum Machines called the OPX1000. The system can be programmed in a language similar to Python, which many physicists already use, making it more accessible to research groups worldwide.

The integration of this controller with advanced quantum hardware was made possible through close collaboration between the Niels Bohr Institute research group led by Associate Professor Morten Kjaergaard and Chalmers University, where the quantum processing unit was designed and fabricated. “The controller enables very tight integration between logic, measurements and feedforward: these components made our experiment possible,” says Morten Kjærgaard.

Why Real Time Calibration Matters for Quantum Computers

Quantum technologies promise powerful new capabilities, though practical large scale quantum computers are still under development. Progress often comes incrementally, but occasionally major steps forward occur.

By uncovering these previously hidden dynamics, the findings reshape how scientists think about testing and calibrating superconducting quantum processors. With current materials and manufacturing methods, moving toward real time monitoring and adjustment appears essential for improving reliability. The results also highlight the importance of partnerships between academic research and industry, along with creative uses of available technology.

“Nowadays, in quantum processing units in general, the overall performance is not determined by the best qubits, but by the worst ones: those are the ones we need to focus on. The surprise from our work is that a ‘good’ qubit can turn into a ‘bad’ one in fractions of a second, rather than minutes or hours.

“With our algorithm, the fast control hardware can pinpoint which qubit is ‘good’ or ‘bad’ basically in real time. We can also gather useful statistics on the ‘bad` qubits in seconds instead of hours or days.

“We still cannot explain a large fraction of the fluctuations we observe. Understanding and controlling the physics behind such fluctuations in qubit properties will be necessary for scaling quantum processors to a useful size,” Fabrizio says.