A new study led by researchers at UCL (University College London) shows that combining quantum computing with artificial intelligence can significantly improve predictions of complex physical systems over long periods. The hybrid approach outperforms leading models that rely only on conventional computers.
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.