mlsanityData to 14 Oct 2026

Reinforcement Learning for Syndrome Extraction

arXiv 2609.12020 ↗First posted 10 Sep 2026cs.LG quant-phcited by 0

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 34 references that are in the map. First posted on arXiv 10 Sep 2026.

Builds on

4 of 34 references.

References · 4

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2016Unifying Count-Based Exploration and Intrinsic Motivationcited by 639
  4. 2022A Deep Reinforcement Learning Approach to Rare Event Estimationcited by 2

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  3. 2026Sparse Mamba Decoder for Quantum Error Correction: Efficient Defect-Centric Processing of Surface Code Syndromes
  4. 2024Reinforcement learning-based architecture search for quantum machine learning
  5. 2024Enhancing Quantum Memory Lifetime with Measurement-Free Local Error Correction and Reinforcement Learning
  6. 2026Context-Aware Error Mitigation Orchestration for Hybrid Quantum Reinforcement Learning on NISQ Systems
  7. 2025Formal Verification of Noisy Quantum Reinforcement Learning Policies
  8. 2026ZeroCode: On-demand Error-Correcting Code Construction from the Zero Matrix via Reinforcement Learning