Optimization of decoder priors for accurate quantum error correction

TL;DR

This paper presents a reinforcement learning inspired method to calibrate decoder priors, significantly improving quantum error correction accuracy on Google's Sycamore processor for repetition and surface codes.

Contribution

It introduces a novel calibration technique for decoder priors that enhances quantum error correction performance, outperforming existing methods.

Findings

16% improvement in repetition code decoding accuracy

3.3% improvement in surface code decoding accuracy

Effective calibration method for near-term quantum devices

Abstract

Accurate decoding of quantum error-correcting codes is a crucial ingredient in protecting quantum information from decoherence. It requires characterizing the error channels corrupting the logical quantum state and providing this information as a prior to the decoder. We introduce a reinforcement learning inspired method for calibrating these priors that aims to minimize the logical error rate. Our method significantly improves the decoding accuracy in repetition and surface code memory experiments executed on Google's Sycamore processor, outperforming the leading decoder-agnostic method by 16% and 3.3% respectively. This calibration approach will serve as an important tool for maximizing the performance of both near-term and future error-corrected quantum devices.