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Optimizing quantum gates towards the scale of logical qubits

Author

Listed:
  • Paul V. Klimov

    (Google AI)

  • Andreas Bengtsson

    (Google AI)

  • Chris Quintana

    (Google AI)

  • Alexandre Bourassa

    (Google AI)

  • Sabrina Hong

    (Google AI)

  • Andrew Dunsworth

    (Google AI)

  • Kevin J. Satzinger

    (Google AI)

  • William P. Livingston

    (Google AI)

  • Volodymyr Sivak

    (Google AI)

  • Murphy Yuezhen Niu

    (Google AI)

  • Trond I. Andersen

    (Google AI)

  • Yaxing Zhang

    (Google AI)

  • Desmond Chik

    (Google AI)

  • Zijun Chen

    (Google AI)

  • Charles Neill

    (Google AI)

  • Catherine Erickson

    (Google AI)

  • Alejandro Grajales Dau

    (Google AI)

  • Anthony Megrant

    (Google AI)

  • Pedram Roushan

    (Google AI)

  • Alexander N. Korotkov

    (Google AI
    University of California)

  • Julian Kelly

    (Google AI)

  • Vadim Smelyanskiy

    (Google AI)

  • Yu Chen

    (Google AI)

  • Hartmut Neven

    (Google AI)

Abstract

A foundational assumption of quantum error correction theory is that quantum gates can be scaled to large processors without exceeding the error-threshold for fault tolerance. Two major challenges that could become fundamental roadblocks are manufacturing high-performance quantum hardware and engineering a control system that can reach its performance limits. The control challenge of scaling quantum gates from small to large processors without degrading performance often maps to non-convex, high-constraint, and time-dynamic control optimization over an exponentially expanding configuration space. Here we report on a control optimization strategy that can scalably overcome the complexity of such problems. We demonstrate it by choreographing the frequency trajectories of 68 frequency-tunable superconducting qubits to execute single- and two-qubit gates while mitigating computational errors. When combined with a comprehensive model of physical errors across our processor, the strategy suppresses physical error rates by ~3.7× compared with the case of no optimization. Furthermore, it is projected to achieve a similar performance advantage on a distance-23 surface code logical qubit with 1057 physical qubits. Our control optimization strategy solves a generic scaling challenge in a way that can be adapted to a variety of quantum operations, algorithms, and computing architectures.

Suggested Citation

  • Paul V. Klimov & Andreas Bengtsson & Chris Quintana & Alexandre Bourassa & Sabrina Hong & Andrew Dunsworth & Kevin J. Satzinger & William P. Livingston & Volodymyr Sivak & Murphy Yuezhen Niu & Trond I, 2024. "Optimizing quantum gates towards the scale of logical qubits," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46623-y
    DOI: 10.1038/s41467-024-46623-y
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    References listed on IDEAS

    as
    1. L. DiCarlo & J. M. Chow & J. M. Gambetta & Lev S. Bishop & B. R. Johnson & D. I. Schuster & J. Majer & A. Blais & L. Frunzio & S. M. Girvin & R. J. Schoelkopf, 2009. "Demonstration of two-qubit algorithms with a superconducting quantum processor," Nature, Nature, vol. 460(7252), pages 240-244, July.
    2. Sebastian Krinner & Nathan Lacroix & Ants Remm & Agustin Paolo & Elie Genois & Catherine Leroux & Christoph Hellings & Stefania Lazar & Francois Swiadek & Johannes Herrmann & Graham J. Norris & Christ, 2022. "Realizing repeated quantum error correction in a distance-three surface code," Nature, Nature, vol. 605(7911), pages 669-674, May.
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