Machine Learning for tuning, controlling, and optimizing semiconductor spin qubits

ORAL  · Invited

Abstract

Quantum computers hold the potential to bring the world into a new quantum age. However, creating and controlling quantum bits (qubits) has turned out to be challenging. In semiconductor spin qubits, the qubit is encoded in the spin degree of freedom of a quantum dot, an electrical potential trap used to confine charge carriers. These quantum dots are controlled with gate voltages which are applied to nanosized gate-electrodes. Despite the vast progress in the quality of the materials hosting these qubits, energizing and tuning semiconductor qubit systems still requires a considerable amount of experience, time, and patience because of device-to-device variations. Here we overcome this by replacing the human operator with automated, AI-based algorithms. Our approach is architecture and material agnostic, which allows us to report results in various spin qubit systems such as Germanium/Silicon core-shell nanowires, silicon finFETs, and Gallium-Arsenide quantum dots.

In the first course tuning step, our machine-learning algorithms find and energize hole and electron quantum dots faster than human experts. Then, supported by a physical model, another algorithm searches a large dimensional parameter space for signatures of spin effects necessary to operate and read out spin qubit systems. Finally, we report on automated quality optimization of an all-electrical hole spin qubit by changing relevant system parameters such as magnetic and electric fields, read-out position, driving strength, and qubit energy.

We believe that such AI-based procedures will be crucial for controlling more extensive and complex spin qubit networks required in a quantum processor.

*This work was supported by the Royal Society, the EPSRC National Quantum Technology Hub in Networked Quantum Information Technology (EP/M013243/1), Quantum Technology Capital (EP/N014995/1), EPSRC Platform (EP/R029229/1), ERC (948932), SNI, NCCR SPIN, EU H2020 EMP (824109), and the Templeton World Charity Foundation.

–

Publication: 1. Identifying Pauli spin blockade using deep learning.
J. Schuff, D.T. Lennon, S. Geyer, D. Craig, F. Fedele, F. Vigneau, L.C. Camenzind, A.V. Kuhlmann, R.J. Warburton, D.M. Zumbühl, D. Sejdinovic, G.A.D. Briggs, N. Ares. Planned Paper (2021).
2. Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning.
B. Severin, D. T. Lennon, L. C. Camenzind, F. Vigneau, F. Fedele, D. Jirovec, A. Ballabio, D. Chrastina, G. Isella, M. de Kruijf, M. J. Carballido, S. Svab, A. V. Kuhlmann, F. R. Braakman, S. Geyer, F. N. M. Froning, H. Moon, M. A. Osborne, D. Sejdinovic, G. Katsaros, D. M. Zumbühl, G. A. D. Briggs, and N. Ares. Preprint, arXiv:2107.12975 (2021).
3. Deep Reinforcement Learning for Efficient Measurement of Quantum Devices.
V. Nguyen*, S. B. Orbell*, D.T. Lennon, H. Moon, F. Vigneau, L.C. Camenzind, L. Yu, D.M. Zumbühl,
G.A.D. Briggs, M. A. Osborne, D. Sejdinovic, and N. Ares. npj Quantum Information 7, 100 (2021).
4. Quantum device fine-tuning using unsupervised embedding learning.
N.M. van Esbroeck, D.T. Lennon, H. Moon, V. Nguyen, F. Vigneau, L.C. Camenzind, L. Yu,
D.M. Zumbühl, G.A.D. Briggs, D. Sejdinovic, and N. Ares. New J. Phys. 22 09503 (2020)
5. Machine learning enables completely automatic tuning of a quantum device faster than human experts.
H. Moon*, D.T. Lennon*, J. Kirkpatrick, N.M. van Esbroeck, L.C. Camenzind, Liuqi Yu, F. Vigneau, D.M. Zumbühl, G.A.D. Briggs, M.A Osborne, D. Sejdinovic, E.A. Laird, N. Ares. Nature Communications 11, 4161 (2020)
6. Efficiently measuring a quantum device using machine learning.
D. T. Lennon, H. Moon, L. C. Camenzind, Liuqi Yu, D. M. Zumbühl, G. A. D. Briggs, M. A. Osborne, E. A. Laird, N. Ares. npj Quantum Information 5, 79 (2019)

Presenters

  • Dominic T Lennon

    • University of Oxford

Authors

  • Leon Camenzind

    • University of Basel

  • Dominic T Lennon

    • University of Oxford

  • Vu Nguyen

    • University of Oxford

  • Brandon Severin

    • University of Oxford

  • Nina M van Esbroeck

    • University of Oxford

  • James Kirkpatrick

    • DeepMind, London, UK

  • Sebastian Orbell

    • University of Oxford

  • Hyungil Moon

    • University of Oxford

  • Jonas Schuff

    • University of Oxford

  • Florian Vigneau

    • University of Oxford

  • Liuqi Yu

    • University of Maryland, College Park
    • University of Basel

  • Simon Geyer

    • University of Basel

  • Andreas V Kuhlmann

    • University of Basel

  • Florian N Froning

    • University of Basel

  • Dino Sejdinovic

    • University of Oxford

  • Michael A Osborne

    • University of Oxford

  • Edward A Laird

    • Lancaster University

  • G. Andrew D Briggs

    • University of Oxford

  • Dominik M Zumbuhl

    • University of Basel

  • Natalia Ares

    • University of Oxford