Fast correlated decoding of transversal logical algorithms

Oral-In-person

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.

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

Presenters

  • Chen Zhao

    • QuEra Computing Inc.

Authors

  • Chen Zhao

    • QuEra Computing Inc.
  • Madelyn Cain

    • Oratomic
  • Dolev Bluvstein

    • Harvard University
  • Shouzhen Gu

    • Yale University
  • Nishad Maskara

    • Harvard University
  • Marcin Kalinowski

    • Harvard University
  • Alexandra Geim

    • Harvard University
  • Aleksander Kubica

    • Yale University
  • Mikhail Lukin

    • Harvard University
  • Hengyun Zhou

    • QuEra Computing and MIT