Nishimori Threshold Estimation for Bayesian Inference and $\mathbb{Z}_q$ Surface Code Decoding

Abstract:In quantum error correction, the error threshold provides essential quantitative guidance for the ability to bring about fault-tolerance through decoding the effects of incoherent noise, weak measurement or inference. However, the numerical value of an error threshold is typically only accessible through large-scale numerical simulations of the underlying noise model. Here we introduce an analytical estimate of error thresholds falling into the Nishimori universality class via a Fourier--Walsh projection scheme that maps the critical point of the underlying disorder-free statistical-mechanics model to the Born-disordered Nishimori critical point. Using a minimal replica theory approach, this closed-form estimate is obtained from a projection of the exact replicated single-bond weight which we find to reproduce (within a percentage point) the known numerical thresholds of random-bond and random-plaquette Ising models / $\mathbb Z_2$ stabilizer codes in spatial dimensions $d=2-5$, and extends to Potts variables with $q\le4$. The main application of our projection scheme is to $\mathbb Z_q$ surface codes, whose decoding problem maps to the disordered $q$-state clock model. For $q\ge5$ the clean clock model has \textit{two} Berezinskii--Kosterlitz--Thouless transitions, which the projection maps to two Nishimori temperatures that bound an intermediate information-critical phase. The resulting threshold values not only accurately agree with recent decohered-$\mathbb Z_q$-toric-code numerics, but are found to satisfy the Gilbert--Varshamov self-dual entropy relation $\ln q \simeq H_q(T_1^\ast)+H_q(T_2^\ast),$ although no duality condition is imposed in the construction. Our approach thereby points to a deeper connection between the clean and Born-disordered models, while allowing for instant analytical estimates of error thresholds for a variety of stabilizer codes.

Quantum Physics (quant-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn); Statistical Mechanics (cond-mat.stat-mech)

arXiv:2607.18374 [quant-ph]

(or arXiv:2607.18374v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2607.18374 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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