A Machine Learning Approach to Superconducting Circuit Design

ORAL

Abstract

Superconducting qubits are electrical circuits comprising inductors, capacitors, and Josephson junctions. Although circuit parameters may in principle assume a wide range of values, existing architectures generally span only a small subset of the available design space, and design variations are often based largely on experience and engineering intuition. However, future circuit architectures – such as radically new qubit designs or many-body couplers for adiabatic quantum computing – will likely extend beyond an intuitive regime. Towards this end, we have developed a numerical search engine that screens the parameter design space to achieve circuits with desired properties. The engine performs Bayesian optimization on a graphical model of arbitrary circuits with several nodes. We present novel circuit designs with targeted spectral properties, robustness to parameter variation, and noise sensitivity. We expect our work to facilitate the design of more complex and robust superconducting circuit architectures.

*This research is funded by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA).

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Presenters

  • Tim Menke

    • Harvard University & Massachusetts Institute of Technology

Authors

  • Tim Menke

    • Harvard University & Massachusetts Institute of Technology

  • Florian Häse

    • Harvard University

  • Simon Gustavsson

    • Massachusetts Institute of Technology
    • Research Laborotary of Electronics, Massachusetts Institute of Technology
    • Massachusetts Inst of Tech-MIT
    • Research Laboratory of Electronics, Massachusetts Institute of Technology
    • Research Laboratory of Electronics, Massachusetts Inst of Tech-MIT
    • MIT
    • Research Laboratory of Electronics, Massachusetts institute of Technology

  • Andrew Kerman

    • MIT Lincoln Laboratory
    • Massachusetts Inst of Tech-MIT
    • MIT Lincoln Lab

  • William Oliver

    • MIT Lincoln Laboratory
    • MIT Lincoln Lab
    • Massachusetts Institute of Technology & MIT Lincoln Laboratory
    • Department of Physics, Research Laboratory of Electronics, Lincoln Laboratory, Massachusetts Institute of Technology
    • Massachusetts Inst of Tech-MIT
    • Department of Physics, Research Laboratory of Electronics, Lincoln Laboratory, Massachusetts Inst of Tech-MIT
    • MIT
    • Lincoln Laboratory, Research Laboratory of Electronics, and Department of Physics, Massachusetts Institute of Technology
    • Department of Physics, Research Laboratory of Electronics, Lincoln Laboratory, Massachusetts institute of Technology

  • Alan Aspuru-Guzik

    • Harvard University
    • Department of Chemistry and Chemical Biology, Harvard University
    • Chemistry and Chemical Biology, Harvard University