Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click on 'Find out more' to see our Cookie statement.

Funded by Innovate UK, the £6.8M project will apply machine learning techniques to find fast, automated, and scalable ways to calibrate quantum computers. The aim is to build a system capable of controlling hundreds of qubits simultaneously across different types of quantum hardware.

Artist's impression of quantum computer

For quantum computers to be used practically, a large number of qubits, or quantum bits, need to be controlled with absolute precision and without errors. This is an extremely difficult task due to the fragility of qubits, which can collapse and lose information due to even the smallest changes in the environment. As a result, quantum computers require constant supervision by highly skilled physicists. Calibration is currently inefficient and time intensive. The cost, time, and effort involved in this process are currently not scalable and constitute a major bottleneck to progressing quantum computing technology.

The quantum device group led by Prof Ares at University of Oxford, in collaboration with machine-learning expert Prof Osborne, pioneered the use of machine learning techniques for quantum device control in real time. Their success in developing quantum device tuning faster than human experts revealed the potential of machine-learning based approaches for the scaling of quantum circuits. Deltaflow Control, a control system being developed by Riverlane, will now build on these techniques to manipulate several qubits simultaneously and will be portable across multiple types of quantum hardware.

Artificial Intelligence specialists Mind Foundry will develop the bespoke machine learning techniques. Machine-learning based qubit calibration will enable faster, more predictable measurements. Users can focus on running useful experiments and get more out of their qubits, instead of spending time and energy on setting qubits up and keeping them from collapsing.  The consortium includes world-leading quantum hardware suppliers Oxford Ionics and SEEQC each representing different types of quantum hardware: trapped ion and superconducting qubits; the University of Oxford will lead the effort on semiconductor qubit circuits, sharing their expertise in tuning such devices through ML-based techniques. The National Physical Laboratory (NPL) and the University of Edinburgh will set the standards for measurement.

According to Prof Natalia Ares at University of Oxford: ‘Every researcher that has characterised and tuned a quantum device has experienced how much experience it requires and how time consuming it can be. When we started a project in collaboration with Prof Osborne to apply machine learning to this challenge, we were not expecting the extent to which we could delegate these tasks to learning algorithms. These have been transformative for my lab and for others labs around the world with which we collaborate. We are now tremendously excited to unleash the potential of these approaches to allow for the scalability of quantum circuits in a vast range of device architectures.’

Similar stories

Oxford zoologists find rare wild ancestors of feral pigeons living on British and Irish islands

DNA testing reveals that rock doves, the wild ancestors of the common domestic and feral pigeons, now extinct in many parts of the world, are still living on islands in Scotland and Ireland.

Next generation of innovators recognised at inaugural Jamie Ferguson Chemistry Innovation Awards

Oxford Chemistry students with innovative, game-changing ideas have been highlighted at the inaugural Jamie Ferguson Chemistry Innovation Awards ('The Jamies'), a new annual awards scheme co-developed by the Department of Chemistry and Oxford University Innovation (OUI) in honour of the late Dr Jamie Ferguson.

Oxford Flight Group reveals how hawks control flight and landing to prioritise safety

Oxford biomechanics researchers have been using computer simulations and Hollywood-style motion capture to reveal how Harris hawks optimise their landing manoeuvres for an accurate descent.

Professor Samuel Sheppard, to join Ineos Oxford Institute to further interdisciplinary research on antimicrobial resistance

Professor Sheppard will join the Department of Biology and the IOI in September 2022, with the aim of using fundamental evolutionary and ecological theory to address consequential questions in pathogen emergence and spread.

Nadja Yang recognised by Women's Engineering Society with 'Top 50 Women in Engineering - Inventors and Innovators' award

DPhil Systems Engineering student, Nadja Yang has been recognised as one of the UK's top 50 women engineers who are creating or significantly improving products or processes to make a difference in the world, on International Women in Engineering Day.