aiqbench analysis
This study introduces argument reweighting, a post-selection decoding strategy that significantly suppresses logical errors in qLDPC codes with minimal resource overhead. By optimizing decoder performance through reweighted error models, the method achieves nearly two orders of magnitude improvement in fault tolerance for specific bicycle codes. These findings offer a practical pathway to enhancing quantum error correction efficiency on near-term hardware.
Simple, Efficient, and Generic Post-Selection Decoding for qLDPC Codes
arXiv:2601.17757v2 Announce Type: replace Abstract: Quantum error correction is indispensable for scalable quantum computation. Although encoding logical qubits substantially enhances noise resilience, achieving logical error rates low enough for practical algorithms remains challenging on existing hardware. Here we introduce argument reweighting, a simple and broadly applicable post-selection decoding strategy that boosts the performance of maximum-likelihood-type decoders, including minimum-weight perfect matching and belief-propagation families. The method suppresses logical errors by performing additional decoding rounds under reweighted error models, enabling acceptance of high-confidence syndrome outcomes. Circuit-level simulations across multiple decoders and qLDPC codes show that argument reweighting substantially suppresses logical errors, requiring a rejection rate of only $1.44\times10^{-5}$ to reduce the logical error rate by almost two orders of magnitude for the $[[144,12,12]]$ bivariate bicycle code. These results establish argument reweighting as a practical and resource-efficient approach for enhancing quantum fault tolerance.
Abstract:Quantum error correction is indispensable for scalable quantum computation. Although encoding logical qubits substantially enhances noise resilience, achieving logical error rates low enough for practical algorithms remains challenging on existing hardware. Here we introduce argument reweighting, a simple and broadly applicable post-selection decoding strategy that boosts the performance of maximum-likelihood-type decoders, including minimum-weight perfect matching and belief-propagation families. The method suppresses logical errors by performing additional decoding rounds under reweighted error models, enabling acceptance of high-confidence syndrome outcomes. Circuit-level simulations across multiple decoders and qLDPC codes show that argument reweighting substantially suppresses logical errors, requiring a rejection rate of only $1.44\times10^{-5}$ to reduce the logical error rate by almost two orders of magnitude for the $[[144,12,12]]$ bivariate bicycle code. These results establish argument reweighting as a practical and resource-efficient approach for enhancing quantum fault tolerance.
arXiv:2601.17757 [quant-ph]
(or arXiv:2601.17757v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2601.17757 Focus to learn more arXiv-issued DOI via DataCite
Phys. Rev. Lett. 137, 110601 (2026)
https://doi.org/10.1103/sv11-f78l Focus to learn more DOI(s) linking to related resources
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