Reinforcement Learning for Syndrome Extraction

2026-09-10

AI Summary

本文使用强化学习和重要性采样方法解决量子纠错中寻找低逻辑错误率的综合症提取方案问题，显著降低了逻辑错误率。

Abstract

A key subtask of quantum error correction is to extract a syndrome that, if nontrivial, signals an error. The number of possible ways to extract a syndrome grows exponentially with the syndrome size, and these implementations vary greatly in fault tolerance, as measured by their logical error rates. This creates a natural search problem: find an implementation with a low logical error rate. Previous work solves this problem but sacrifices either solution quality or scalability. In this paper, we use reinforcement learning and importance sampling to outperform previous work at all scales. Compared with the state of the art automatic scheduling tools AlphaSyndrome and PropHunt, our tool reduces the logical error rate by 25.9\% and 71.7\% on average, respectively, culminating with a reduction of 97.8\% for a surface code with distance 15.

Problem

• quantum error correction
• syndrome extraction
• logical error rate

Innovation

• reinforcement learning
• importance sampling
• quantum error correction
• syndrome extraction