Towards real time, low latency, circuit level decoding of QLDPC codes

ORAL

Abstract

We extend Astra, our graph neural network approach for decoding QLDPC codes, and showcase the scalability and low latency performance for circuit level depolarizing noise. Previous work [https://arxiv.org/abs/2408.07038] demonstrated that for code capacity depolarizing noise, Astra is outperforming the BP+OSD decoder on accuracy and speed. Moreover, without sacrificing decoding performance, our decoder can perform extrapolated decoding, i.e. it can decode high distances using models trained on lower distances. Herein, we present results for both surface codes and bivariate bicycle codes. We perform latency and hardware acceleration studies of our decoder, and illustrate a convincing path towards Astra’s capability for real time decoding.

*This research was developed in part with funding from the Defense Advanced Research Projects Agency [under the Quantum Benchmarking (QB) program under award no. HR00112230006 and HR001121S0026 contracts], and was supported by the QuantERA grant EQUIP through the Academy of Finland, decision number 352188. The views, opinions and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. We acknowledge the computational resources provided by the Aalto Science IT project.

Presenters

  • Arshpreet S Maan

    • Aalto University

Authors

  • Arshpreet S Maan

    • Aalto University
  • Alexandru Paler

    • Aalto University