Scalable Quantum Computing on a Noisy Superconducting Quantum Processor via Randomized Compiling

ORAL

Abstract

Coherent errors in quantum hardware severely limit the performance of quantum algorithms in an unpredictable manner, and mitigating their impact is necessary for realizing reliable, large-scale quantum computations. Randomized compiling achieves this goal by converting coherent errors into stochastic noise, dramatically reducing unpredictable errors in quantum algorithms and enabling accurate predictions of aggregate performance via cycle benchmarking estimates. In this work, we demonstrate significant performance gains under randomized compiling for both the four-qubit quantum Fourier transform algorithm and for random circuits of variable depth on a superconducting quantum processor. We also validate solution accuracy using experimentally-measured error rates. Our results demonstrate that randomized compiling can be utilized to maximally-leverage and predict the capabilities of modern-day noisy quantum processors, paving the way forward for scalable quantum computing.

*This work was Funded by the Office of Advanced Scientific Computing Research, Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. A.H. acknowledges financial support from the National Defense Science & Engineering Graduate (NDSEG) Fellowship.

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Presenters

  • Akel Hashim

    • Univ of California – Berkeley
    • University of California, Berkeley
    • Quantum Nanoelectronics Lab, UC Berkeley
    • University of California - Berkeley

Authors

  • Akel Hashim

    • Univ of California – Berkeley
    • University of California, Berkeley
    • Quantum Nanoelectronics Lab, UC Berkeley
    • University of California - Berkeley

  • Ravi K. Naik

    • University of California, Berkeley
    • Univ of California – Berkeley
    • Physics, University of California, Berkeley
    • University of California Berkeley
    • Univ of California - Berkeley
    • Quantum Nanoelectronics Laboratory, Dept. of Physics, University of California, Berkeley
    • University of California - Berkeley

  • Alexis Morvan

    • University of California, Berkeley
    • Lawrence Berkeley National Laboratory
    • Laboratoire de Physique des Solides, CNRS - Université Paris Saclay

  • Jean-Loup Ville

    • University of California, Berkeley
    • University of California - Berkeley

  • Brad Mitchell

    • University of California, Berkeley
    • Univ of California – Berkeley
    • University of California - Berkeley

  • John Mark Kreikebaum

    • Lawrence Berkeley National Laboratory
    • University of California, Berkeley
    • Univ of California – Berkeley
    • Physics, University of California, Berkeley

  • Marc Davis

    • Lawrence Berkeley National Laboratory

  • Ethan Smith

    • University of California Berkeley
    • Lawrence Berkeley National Laboratory

  • Costin Iancu

    • Lawrence Berkeley National Lab
    • Lawrence Berkeley National Laboratory

  • Kevin O'Brien

    • Massachusetts Institute of Technology MIT
    • Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
    • Univ of California – Berkeley
    • Massachusetts Institute of Technology

  • Ian Hincks

    • Quantum Benchmark, Inc.

  • Joel Wallman

    • University of Waterloo
    • Quantum Benchmark, Inc.

  • Joseph V Emerson

    • Quantum Benchmark, Inc.

  • David Ivan Santiago

    • Lawrence Berkeley National Laboratory
    • University of California, Berkeley
    • Lawrence Berkely National Laboratory
    • Quantum Nanoelectronics Laboratory, Dept. of Physics, University of California, Berkeley

  • Irfan Siddiqi

    • Lawrence Berkeley National Laboratory
    • University of California, Berkeley
    • Univ of California - Berkeley
    • Univ of California – Berkeley
    • Quantum Nanoelectronics Lab, UC Berkeley
    • Physics, University of California, Berkeley
    • Quantum Nanoelectronics Laboratory, Dept. of Physics, University of California, Berkeley