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Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning

ORAL

Abstract

The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions. We give a key step towards tackling this variability with an algorithm that, without modification, is capable of tuning a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate Ge/SiGe heterostructure double quantum dot device from scratch. We achieve tuning times of 30, 10, and 92 minutes, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.

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Publication: arXiv:2107.12975 [cond-mat.mes-hall]

Presenters

  • Brandon Severin

    University of Oxford

Authors

  • Brandon Severin

    University of Oxford

  • Dominic T Lennon

    University of Oxford

  • Leon C Camenzind

    RIKEN Center for Emergent Matter Science (CEMS), Wako, Japan, University of Basel, Switzerland; RIKEN Center for Emergent Matter Science (CEMS), Wako, Japan, University of Basel

  • Florian Vigneau

    University of Oxford, University of Oxford Materials Department

  • Federico Fedele

    Niels Bohr Institute, University of Copenhagen, University of Oxford, University Of Oxford

  • Daniel Jirovec

    Institute of Science and Technology Austria

  • Andrea Ballabio

    L-NESS, Physics Department, Politecnico di Milano, Politecnico di Milano, L-NESS, Physics Department, Politecnico di Milano, 22100 Como, Italy, L-NESS, Physics Department, Politecnico di Milano, via Anzani 42, 22100, Como, Italy, L-NESS, Politecnico di Milano

  • Daniel Chrastina

    L-NESS, Physics Department, Politecnico di Milano, Politecnico di Milano, L-NESS, Physics Department, Politecnico di Milano, 22100 Como, Italy, L-NESS, Physics Department, Politecnico di Milano, via Anzani 42, 22100, Como, Italy, L-NESS, Politecnico di Milano

  • Giovanni Isella

    L-NESS, Physics Department, Politecnico di Milano, Politecnico di Milano, L-NESS, Physics Department, Politecnico di Milano, 22100 Como, Italy, L-NESS, Politecnico di Milano

  • Mathieu de Kruijf

    University of Basel

  • Miguel J Carballido

    University of Basel

  • Simon Svab

    University of Basel

  • Andreas V Kuhlmann

    University of Basel

  • Floris Braakman

    University of Basel

  • Simon Geyer

    University of Basel

  • Florian N Froning

    University of Basel

  • Hyungil Moon

    University of Oxford

  • Michael A Osborne

    University of Oxford

  • Dino Sejdinovic

    University of Oxford

  • Georgios Katsaros

    Institute of Science and Technology Austria, IST Austria, Institute of Science and Technology Austria (ISTA), 3400 Klosterneuburg, Austria, Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria

  • Dominik M Zumbuhl

    University of Basel

  • G. Andrew D Briggs

    University of Oxford

  • Natalia Ares

    University of Oxford