Reinforcement Learning for Syndrome Extraction

The paper presents a reinforcement learning approach to the subtask of quantum error correction known as syndrome extraction. By combining reinforcement learning with importance sampling, the authors achieve lower logical error rates across all tested scales compared to existing tools AlphaSyndrome and PropHunt. Their method reduces logical error rates by 25.9% and 71.7% on average, reaching a 97.8% reduction for a surface code of distance 15.

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• reinforcement-learning