Manufacturing a Quantum Computer using Artificial Intelligence

Part of the work of the Electronic Materials and Devices group focuses on integrating artificial intelligence (AI) to develop innovative approaches to building quantum computers. This research pushes the boundaries of current technological capabilities and promises a new era of computing.

Collaborating closely with Nanolayers Research Computing, a leading UK-based company specialising in machine learning and materials discovery, the UCL team leverages AI to enhance atomically-precise fabrication, and improve efficiency when producing components that could eventually be used in a quantum computer. Nanolayers’ expertise in machine learning is critical to developing procedures to reproducibly fabricate the intricate components needed for quantum computers.

The efforts between UCL and Nanolayers blend theoretical research and practical application. Nanolayers provides cutting-edge AI algorithms to optimise the manufacturing process, while the UCL team applies these algorithms to their state-of-the-art atomic scale fabrication facilities to produce silicon-based electronics with unprecedented accuracy.

“By partnering with Nanolayers and tapping into their expertise in AI, we have been able to jump start the introduction of machine learning assistance to our atomically precise fabrication process. This brings us one step closer to the end goal of atomically-perfect electronic device structures, and the fabrication of a universal solid-state quantum computer,” Dr Taylor Stock stated.

A key focus of this partnership is the development of AI-driven techniques that will reduce errors in the manufacturing process. Using machine learning models, the team is developing the capability to predict and mitigate potential defects in real-time, ensuring each quantum chip meets the highest standards of quality.

“Our goal of building a universal quantum computer in silicon will require us to fabricate each active quantum element (called a qubit) with a control and precision that has never before been achieved, and to do this many times over. Our laboratory is uniquely positioned to reproducibly engineer such qubits, and Nanolayers machine learning expertise will help us scale up the number of qubits to the huge number we need,” said Prof Neil Curson.

Additionally, this work could also find application in semiconductor manufacturing. Similar AI methodologies to those developed here, could be used to enhance the production of conventional semiconductors, leading to more efficient and cost-effective processes within industry.

UCL Electronic and Electrical Engineering is passionate about creating real-world impact, with a track record of industry collaboration dating back to 1899, when John Ambrose Fleming became Scientific Advisor to the Marconi Company. Our globally pioneering research is still at the forefront of innovation. We build on our long-established links with industry through collaborative and sponsored research, and industry-supervised student projects.

Interested in collaborating with UCL Electronic and Electrical Engineering? Check out our collaboration pages to find out more and get involved.