Fast correlated decoding of transversal logical algorithms

ORAL

Abstract

Quantum error correction (QEC) is required for large-scale computation, but incurs a significant resource overhead. Recent advances have shown that by jointly decoding logical qubits in algorithms composed of transversal gates, the number of syndrome extraction rounds can be reduced by a factor of the code distance d, at the cost of increased classical decoding complexity. Here, we reformulate the problem of decoding transversal circuits by directly decoding relevant logical operator products as they propagate through the circuit. This procedure transforms the decoding task into one closely resembling that of a single-qubit memory propagating through time. The resulting approach leads to fast decoding and reduced problem size while maintaining high performance. Focusing on the surface code, we prove that this method enables fault-tolerant decoding with minimum-weight perfect matching, and benchmark its performance on example circuits including magic state distillation. We find that the threshold is comparable to that of a single-qubit memory, and that the total decoding run time can be, in fact, less than that of conventional lattice surgery. Our approach enables fast correlated decoding, providing a pathway to directly extend single-qubit QEC techniques to transversal algorithms.

*We acknowledge financial support from IARPA and the Army Research Office, under the Entangled Logical Qubits program (Cooperative Agreement Number W911NF-23-2-0219), the DARPA ONISQ program (grant number W911NF2010021), the DARPA MeasQuIT program (grant number HR0011-24-9-0359), the Center for Ultracold Atoms (a NSF Physics Frontiers Center, PHY-1734011), the National Science Foundation (grant numbers PHY-2012023 and  CCF-2313084), the NSF EAGER program (grant number CHE-2037687), the Army Research Office MURI (grant number W911NF-20-1-0082), the Army Research Office (award number W911NF2320219 and grant number W911NF-19-1-0302), the Wellcome Leap Quantum for Bio program, and QuEra Computing. D.B. acknowledges support from the NSF Graduate Research Fellowship Program (grant DGE1745303) and The Fannie and John Hertz Foundation.

Publication: https://arxiv.org/abs/2505.13587

Presenters

  • Chen Zhao

    • QuEra Computing Inc.

Authors

  • Chen Zhao

    • QuEra Computing Inc.
  • Madelyn Cain

    • Harvard University
  • Dolev Bluvstein

    • Harvard University
    • California Institute of Technology
  • Shouzhen Gu

    • Yale University
  • Nishad Maskara

    • Harvard University
  • Marcin Kalinowski

    • Harvard University
  • Alexandra A Geim

    • Harvard University
  • Aleksander M Kubica

    • Yale University
    • Yale
  • Mikhail D Lukin

    • Harvard University
  • Hengyun Zhou

    • QuEra Computing Inc.
    • QuEra Computing Inc., Massachusetts Institute of Technology
    • QuEra Computing and MIT