Topological error correcting codes, and particularly the surface code, currently provide the most feasible road-map towards large-scale fault-tolerant quantum computation. As such, obtaining fast and flexible decoding algorithms for these codes, within the experimentally realistic and challenging context of faulty syndrome measurements, without requiring any final read-out of the physical qubits, is of critical importance. In this work, we show that the problem of decoding such codes can be naturally reformulated as a process of repeated interactions between a decoding agent and a code environment, to which the machinery of reinforcement learning can be applied to obtain decoding agents. While in principle this framework can be instantiated with environments modelling circuit level noise, we take a first step towards this goal by using deepQ learning to obtain decoding agents for a variety of simplified phenomenological noise models, which yield faulty syndrome measurements without including the propagation of errors which arise in full circuit level noise models.

拓扑纠错码，尤其是表面码，目前为大规模容错量子计算提供了最可行的路线图。这样，在错误的综合症测量结果的实验​​现实且具有挑战性的上下文中，而无需任何最终读取物理量子位的情况下，获得针对这些代码的快速且灵活的解码算法至关重要。在这项工作中，我们表明，解码此类代码的问题可以自然地重新定义为解码代理与代码环境之间反复交互的过程，可以将强化学习机制应用于该过程以获取解码代理。虽然原则上可以使用模拟电路级噪声的环境实例化此框架，