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.

*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 Grant (EP/R029229/1), the European Research Council (Grant agreement 948932), the Swiss Nanoscience Institute, the NCCR SPIN, the EU H2020 European Microkelvin Platform EMP grant No. 824109, the Scientific Service Units of IST Austria through resources provided by the nanofabrication facility, the FWF-I 05060 and the FWF-P 30207 project.

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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