Research claims quantum boost for computer efficiency

Research published in Science Advances referenced the device’s capacity to more efficiently hold very large amounts of information.

Whereas bits in a conventional computer are switched on or off, 1 or 0, quantum computer qubits can be 1, 0, or any state in between. As each qubit can affect any other qubits, even a few can generate large numbers of possible states.

While much attention has focussed on the potential benefits of quantum devices, the challenge has been to demonstrate their practical applications.

Joint first author, Xiao Xue, based at the Centre for Advanced Research Computing (ARC) at UCL, said: “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.”

The research team employed a quantum device at only one stage of the process; by not moving between classical and quantum systems, they avoided the challenges of excessive noise, errors and interference that necessitate the need for too many measurements.

A 20-qubit IQM quantum computer was used and linked to conventional supercomputing resources at Germany’s Leibniz Supercomputing Centre.

Data about a complex system was fed to a quantum computer which learned the key statistical patterns of the data, or the invariant statistical properties, that could then be incorporated into the training of the AI model via the conventional supercomputer

Senior author professor Peter Coveney explained how the method might improve models predicting how liquids and gases move and interact .

“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,” he said.

“Our method can be used in climate forecasting, in modelling blood flow and the interaction of molecules, or to better design wind farms so they generate more energy.”

The research team said the quantum-informed method was about a fifth more accurate –  and remained stable over the long term in its predictions of how a complex, chaotic system would behave – compared to the AI model that did not use the quantum-learned patterns, and required far less memory.

First author Maida Wang 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.

“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.”

To achieve their quantum state, the computers must be cooled to -273°C.

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

Pic (IQM): IQM-20-qubit quantum computer used for the research