图神经网络后选择用于量子纠错
Graph Neural Post-selection for Quantum Error Correction
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中文总结 AI 辅助
本文提出用图神经网络基于综合征预测量子纠错解码器失败，通过后选择保留90%射击，显著降低逻辑错误率，且无需额外解码执行。
中文摘要 AI 辅助
后选择通过拒绝可能导致逻辑失败的射击来提高量子计算的逻辑可靠性。我们引入了图神经网络，仅使用综合征预测解码器失败，无需额外的解码器执行，并且对于高码率量子低密度奇偶校验（qLDPC）码，利用现有的置信传播后验。我们在均匀去极化电路级噪声下评估了旋转表面码和双变量自行车（BB）码存储器。保留90%的射击，在现实物理错误率下，[[72,12,6]]和[[144,12,12]] BB码的逻辑错误率分别降低了约3700倍和740倍。对于距离为5的表面码，在物理噪声p=0.003时，我们的神经后选择器实现了与领先的互补间隙方法相当的逻辑错误抑制，而无需运行解码器。
英文摘要
Post-selection improves the logical reliability of quantum computation by rejecting shots which are likely to result in logical failure. We introduce graph neural networks that predict decoder failure without additional decoder executions, using only syndromes, and for high-rate quantum low-density parity-check (qLDPC) codes, existing belief-propagation posteriors. We evaluate rotated surface and bivariate bicycle (BB) code memories under uniform depolarising circuit-level noise. Retaining 90% of shots gives approximately 3700x and 740x reductions in logical error rates for [[72,12,6]] and [[144,12,12]] BB codes at realistic physical error rates. For the distance 5 surface code at physical noise p=0.003, our neural post-selector achieves logical error suppression comparable to the leading complementary gap method without running the decoder.
发表机构
- Imperial College London（帝国理工学院）
- The University of Edinburgh（爱丁堡大学）
机构由 AI 辅助整理，请以论文原文为准。
