Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Iterative Decoding of Stabilizer Codes under Radiation-Induced Correlated Noise

Anuj K. Nayak, Paul G. Baity, Peter J. Love, Nicholas Jeon, Byung-Jun Yoon, Adolfy Hoisie, Lav R. Varshney

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.

Iterative Decoding of Stabilizer Codes under Radiation-Induced Correlated Noise

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.
Paper Structure (43 sections, 42 equations, 14 figures, 2 tables, 2 algorithms)

This paper contains 43 sections, 42 equations, 14 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Radiation-Induced Correlated Noise
  4. Quantum Error Correction
  5. Stabilizer Codes
  6. Model
  7. QP Density Evolution
  8. Circuit Noise Model
  9. Detector Error Model
  10. Probabilistic Graphical Model
  11. Decoding
  12. Belief Propagation
  13. Post-Processing: Ordered Statistics Decoding
  14. Problem Formulation
  15. Variational E-step (Decoding)
  16. ...and 28 more sections

Figures (14)

  • Figure 1: (a) Distance-7 surface code layout on a $40 \rm{mm} \times 40 \rm{mm}$ superconducting chip (shown at time slice $0~\mu s$) along with time varying QP density evolution heatmap; (b) Logical error probability (PLE) of decoder with perfect knowledge of instantaneous QP density and of the same decoder with uniform prior. The gap in PLE suggests room for improvement.
  • Figure 2: Graphical model coupling QP density evolution, circuit faults, error mechanisms and detection events.
  • Figure 3: Distance 7 surface code layout (left) and time-averaged QP density of muon strike sample 58 (right).
  • Figure 4: [[72, 12, 6]] BB qLDPC layout (left) and time-aveaged QP density of muon strike sample 53 (right).
  • Figure 5: QP density estimation error of the proposed algorithms.
  • ...and 9 more figures