A non-linear sigma model maps surface-code decoding under coherent errors to distinct replica limits, exposing a thermal-metal phase for suboptimal decoders that is absent in optimal decoding.
Available: https://arxiv.org/abs/2101.04125
6 Pith papers cite this work, alongside 26 external citations. Polarity classification is still indexing.
6
Pith papers citing it
26
external citations · external index
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Readout errors, including correlated ones, are characterized and mitigated with a tensor-network (MPO/PEPO) model trained from calibration data.
The Frontier decoder approximates optimal quantum LDPC decoding via narrow-frontier dynamic programming, achieving near-optimal thresholds for surface and color codes plus state-of-the-art circuit-level performance with small retained lists.
Numerical simulations of the surface-code ML decoder under single- and two-qubit unitary rotations reveal a ferromagnetic volume-law phase in which classical information is retained yet hard to recover.
A distributed (6.6.6) color code is realized by interconnecting patches via entangled pairs, with simulations showing the concatenated MWPM decoder maintains error threshold under asymmetric seam noise while tensor-network decoder shows slight reduction.
A topical review unifying statistical mechanics, tensor network, and AI approaches to approximate maximum likelihood decoding for quantum error correction codes.
citing papers explorer
-
Non-linear Sigma Model for the Surface Code with Coherent Errors
A non-linear sigma model maps surface-code decoding under coherent errors to distinct replica limits, exposing a thermal-metal phase for suboptimal decoders that is absent in optimal decoding.
-
Tensor network characterization and mitigation of readout errors
Readout errors, including correlated ones, are characterized and mitigated with a tensor-network (MPO/PEPO) model trained from calibration data.
-
Approximating optimal decoding of quantum LDPC codes with narrow frontiers
The Frontier decoder approximates optimal quantum LDPC decoding via narrow-frontier dynamic programming, achieving near-optimal thresholds for surface and color codes plus state-of-the-art circuit-level performance with small retained lists.
-
Phases of decodability in the surface code with unitary errors
Numerical simulations of the surface-code ML decoder under single- and two-qubit unitary rotations reveal a ferromagnetic volume-law phase in which classical information is retained yet hard to recover.
-
Distributed Realization of Color Codes for Quantum Error Correction
A distributed (6.6.6) color code is realized by interconnecting patches via entangled pairs, with simulations showing the concatenated MWPM decoder maintains error threshold under asymmetric seam noise while tensor-network decoder shows slight reduction.
-
Maximum Likelihood Decoding of Quantum Error Correction Codes
A topical review unifying statistical mechanics, tensor network, and AI approaches to approximate maximum likelihood decoding for quantum error correction codes.