Deep Learning-Based Prediction and Optimal Sequential Measurement of a Quantum Dot

POSTER

Abstract

Spin qubits defined in quantum dots are promising for creating a scalable quantum computer. However, they are time-consuming to characterise, and as the size of these systems increases, this task will become intractable without the aid of automation. We present a machine learning algorithm that decides where to measure next and demonstrate it operating on a real quantum dot device in real-time. The algorithm utilises a probabilistic deep-generative model to make reconstructions of a full current map given partial measurement and information theory to select the most informative measurements to perform next.
We demonstrate, for two different measurement configurations, that the algorithm outperforms standard grid scan techniques, reducing the number of measurements required by up to 4 times and the measurement time by 3.7 times.

Presenters

  • Dominic Lennon

    Materials, University of Oxford

Authors

  • Dominic Lennon

    Materials, University of Oxford

  • Hyungil Moon

    Materials, University of Oxford

  • Michael Osborne

    Department of Engineering, University of Oxford

  • Leon Camenzind

    University of Basel, Department of Physics, University of Basel

  • Liuqi Yu

    Laboratory for Physical Sciences, College Park, MD, University of Basel, Department of Physics, University of Basel

  • Dominik Zumbuhl

    University of Basel, Department of Physics, Univ of Basel, University of Basel, Department of Physics, Department of Physics, University of Basel, Physics, University of Basel

  • George Andrew Davidson Briggs

    Department of Materials, University of Oxford, Oxford University-USE 4643, Materials, University of Oxford

  • Edward Laird

    Department of Physics, Lancaster University, Physics, Lancaster University