Correlated decoding of logical algorithms with transversal gates

ORAL

Abstract

Quantum error correction is essential to perform reliable quantum computation at scale. Recent experiments have realized error-corrected quantum algorithms on a multi-qubit logical processor, crucially relying on the use of transversal gates. Here we show that the performance of algorithms with transversal gates can be substantially improved by accounting for physical error propagation during transversal gates and decoding the logical qubits jointly. We find that this correlated decoding significantly improves the performance of both Clifford and non-Clifford transversal entangling gates, and we explore two decoders offering different computational runtimes and accuracies. We then apply correlated decoding to deep logical circuits with noisy syndrome extraction and find that both the logical error rate and the spacetime overhead can be significantly reduced by utilizing this technique to reduce the number of rounds of noisy syndrome extraction per gate. This correlated decoding technique offers key advantages in early fault-tolerant computation, as well as the possibility for reduction in the spacetime cost of logical algorithms.

*We acknowledge financial support from the DARPA ONISQ program (grant number W911NF2010021), the US Department of Energy (DOE Quantum Systems Accelerator Center, contract number 7568717 and DE-SC0021013), the Center for Ultracold Atoms, the National Science Foundation, the Army Research Office MURI (grant number W911NF-20-1-0082), the Army Research Office (award number W911NF2320219), and QuEra Computing. D.B. acknowledges support from the NSF Graduate Research Fellowship Program (grant DGE1745303) and The Fannie and John Hertz Foundation. M.C. acknowledges support from Department of Energy Computational Science Graduate Fellowship under Award Number DE-SC0020347. J.P.B.A. acknowledges support from the Generation Q G2 fellowship and the Ramsay Centre for Western Civilisation.

Presenters

  • Nadine Meister

    • Harvard University

Authors

  • Nadine Meister

    • Harvard University
  • Madelyn Cain

    • Harvard University
  • Chen Zhao

    • Harvard
  • Hengyun Zhou

    • Harvard University & QuEra Computing
  • Pablo Bonilla Ataides

    • Harvard
  • Dolev Bluvstein

    • Harvard University
  • Mikhail D Lukin

    • Harvard University