Iterative Decoding of Stabilizer Codes under Radiation-Induced Correlated Noise

TL;DR

This paper introduces an iterative decoding method for stabilizer codes that jointly estimates radiation-induced correlated noise and decodes, improving logical error rates in quantum computing.

Contribution

It presents a novel joint noise sensing and decoding algorithm using a variational EM approach for correlated noise in quantum error correction.

Findings

Reduced logical error probability in simulations

Effective estimation of quasiparticle density as diagnostic tool

Improved decoding performance over baseline methods

Abstract

Fault-tolerant quantum computation demands extremely low logical error rates, yet superconducting qubit arrays are subject to radiation-induced correlated noise arising from cosmic-ray muon-generated quasiparticles. The quasiparticle density is unknown and time-varying, resulting in a mismatch between the true noise statistics and the priors assumed by standard decoders, and consequently, degraded logical performance. We formalize joint noise sensing and decoding using syndrome measurements by modeling the QP density as a latent variable, which governs correlation in physical errors and syndrome measurements. Starting from a variational expectation--maximization approach, we derive an iterative algorithm that alternates between QP density estimation and syndrome-based decoding under the updated noise model. Simulations of surface-code and bivariate bicycle quantum memory under radiation-induced correlated noise demonstrate a measurable reduction in logical error probability relative to baseline decoding with a uniform prior. Beyond improved decoding performance, the inferred QP density provides diagnostic information relevant to device characterization, shielding, and chip design. These results indicate that integrating physical noise estimation into decoding can mitigate correlated noise effects and relax effective error-rate requirements for fault-tolerant quantum computation.