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Statistical mechanical models for quantum codes with correlated noise

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arxiv 1809.10704 v2 pith:BDYTRAGT submitted 2018-09-27 quant-ph cond-mat.stat-mech

classification quant-phcond-mat.stat-mech
keywords modelsnoisecodescorrelatedmappingmechanicaloptimalstatistical
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

We give a broad generalisation of the mapping, originally due to Dennis, Kitaev, Landahl and Preskill, from quantum error correcting codes to statistical mechanical models. We show how the mapping can be extended to arbitrary stabiliser or subsystem codes subject to correlated Pauli noise models, including models of fault tolerance. This mapping connects the error correction threshold of the quantum code to a phase transition in the statistical mechanical model. Thus, any existing method for finding phase transitions, such as Monte Carlo simulations, can be applied to approximate the threshold of any such code, without having to perform optimal decoding. By way of example, we numerically study the threshold of the surface code under mildly correlated bit-flip noise, showing that noise with bunching correlations causes the threshold to drop to $p_{\textrm{corr}}=10.04(6)\%$, from its known iid value of $p_{\text{iid}}=10.917(3)\%$. Complementing this, we show that the mapping also allows us to utilise any algorithm which can calculate/approximate partition functions of classical statistical mechanical models to perform optimal/approximately optimal decoding. Specifically, for 2D codes subject to locally correlated noise, we give a linear-time tensor network-based algorithm for approximate optimal decoding which extends the MPS decoder of Bravyi, Suchara and Vargo.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Confinement as Decoding: Higher Form Codes and Lattice Yang-Mills Theory

    hep-th 2026-08 conditional novelty 6.0 of 10

    Decoding a higher-form quantum code with Wilson-line noise is the same computation as comparing center-twisted Yang-Mills partition functions; the paper works out this dictionary and its strong-coupling consequences.

  2. Tensor-network decoders for process tensor descriptions of non-Markovian noise

    quant-ph 2024-12 conditional novelty 6.0 of 10

    A tensor-network-based maximum likelihood decoder is constructed for quantum error correction under process-tensor noise, with an MPS approximation demonstrated on the five-qubit and Steane codes.

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