Iterative Decoding of Stabilizer Codes under Radiation-Induced Correlated Noise
🤖 AI Summary
Superconducting qubit arrays are highly susceptible to quasiparticle bursts induced by cosmic rays, which degrade the performance of conventional quantum error correction decoders due to mismatched noise priors. This work proposes an iterative decoding approach that integrates physical noise modeling directly into the decoding process, formulated within a variational expectation-maximization framework. By treating the quasiparticle density as a latent variable, the method alternately updates the noise model and decoding estimates based on syndrome measurements, enabling adaptive handling of time-varying correlated noise. Simulations on both surface codes and bivariate bicycle codes demonstrate that the proposed decoder significantly reduces logical error rates compared to uniform-prior baselines and simultaneously yields accurate estimates of quasiparticle densities—information valuable for optimizing chip design and radiation shielding strategies.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
correlated noise
radiation-induced errors
quantum error correction
quasiparticle density
fault-tolerant quantum computation
Innovation
Methods, ideas, or system contributions that make the work stand out.
iterative decoding
correlated noise
quasiparticle density estimation
variational EM
fault-tolerant quantum computation
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