TL;DR
SAQ-Decoder is a novel quantum error correction decoder that combines transformer-based learning with constraint-aware post-processing, achieving near-ML accuracy and linear scalability.
Contribution
It introduces a unified framework that integrates dual-stream transformers and a differentiable logical loss for efficient, accurate quantum error decoding.
Findings
Achieves error thresholds close to maximum likelihood bounds.
Outperforms existing neural and classical decoders in accuracy and efficiency.
Demonstrates that learned decoders can be both accurate and scalable.
Abstract
Quantum Error Correction (QEC) decoding faces a fundamental accuracy-efficiency tradeoff. Classical methods like Minimum Weight Perfect Matching (MWPM) exhibit variable performance across noise models and suffer from polynomial complexity, while tensor network decoders achieve high accuracy but at prohibitively high computational cost. Recent neural decoders reduce complexity but lack the accuracy needed to compete with computationally expensive classical methods. We introduce SAQ-Decoder, a unified framework combining transformer-based learning with constraint aware post-processing that achieves both near Maximum Likelihood (ML) accuracy and linear computational scalability with respect to the syndrome size. Our approach combines a dual-stream transformer architecture that processes syndromes and logical information with asymmetric attention patterns, and a novel differentiable logical…
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