Maximally-Matched Mixture Decoding for Correlated Atom Loss in Quantum LDPC Codes
Abstract
Heralded atom loss is a dominant noise channel in neutral atom quantum computing platforms, and losses at a two-qubit gate are strongly correlated: when one atom is lost, its gate partner is often lost too. This work shows how to exploit this correlation to construct a better decoder, focusing on [[144,12,12]] Gross code syndrome extraction circuits. Given a loss readout, the possible pairings of the lost qubits according to shared gates form a graph whose maximum matchings carry almost all of the conditional probability mass. Our maximal-matching-mixture (MMM) decoder builds a per-readout decoding hypergraph as a weighted mixture over exactly these loss scenarios. This decoding hypergraph can be used by an ordinary belief-propagation decoder. On the [[144,12,12]] Gross code, MMM lowers the logical error rate by as much as ~25x relative to a loss-unaware decoder and ~2x relative to a loss-aware uniform-mixture baseline from Baranes et al. (2026).
Related
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BibTeX
@misc{murri2026maximallymatched,
title = {{Maximally-Matched Mixture Decoding for Correlated Atom Loss in Quantum LDPC Codes}},
author = {Jacob W. Murri},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.38283},
eprint = {2610.38283},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.38283},
}
RIS
TY - UNPB
TI - Maximally-Matched Mixture Decoding for Correlated Atom Loss in Quantum LDPC Codes
AU - Jacob W. Murri
PY - 2026
DA - 2026/10/28
PB - arXiv
DO - 10.48550/arxiv.2610.38283
UR - https://arxiv.org/abs/2610.38283
AB - Heralded atom loss is a dominant noise channel in neutral atom quantum computing platforms, and losses at a two-qubit gate are strongly correlated: when one atom is lost, its gate partner is often lost too. This work shows how to exploit this correlation to construct a better decoder, focusing on [[144,12,12]] Gross code syndrome extraction circuits. Given a loss readout, the possible pairings of the lost qubits according to shared gates form a graph whose maximum matchings carry almost all of the conditional probability mass. Our maximal-matching-mixture (MMM) decoder builds a per-readout decoding hypergraph as a weighted mixture over exactly these loss scenarios. This decoding hypergraph can be used by an ordinary belief-propagation decoder. On the [[144,12,12]] Gross code, MMM lowers the logical error rate by as much as ~25x relative to a loss-unaware decoder and ~2x relative to a loss-aware uniform-mixture baseline from Baranes et al. (2026).
ER -