In the ongoing race towards experimental implementations of quantum error correction (QEC), finding ways to automatically discover codes and encoding strategies tailored to the qubit hardware platform is emerging as a critical problem. Reinforcement learning (RL) has been identified as a promising approach, but so far it has been severely restricted in terms of scalability. In this work, we significantly expand the power of RL approaches to QEC code discovery. Explicitly, we train an RL agent that automatically discovers both QEC codes and their encoding circuits for a given gate set, qubit connectivity and error model, from scratch. This is enabled by a reward based on the Knill-Laflamme conditions and a vectorized Clifford simulator, showing its effectiveness with up to 25 physical qubits and distance 5 codes, while presenting a roadmap to scale this approach to 100 qubits and distance 10 codes in the near future. We also introduce the concept of a noise-aware meta-agent, which learns to produce encoding strategies simultaneously for a range of noise models, thus leveraging transfer of insights between different situations. Our approach opens the door towards hardware-adapted accelerated discovery of QEC approaches across the full spectrum of quantum hardware platforms of interest.

在量子纠错 （QEC） 的实验性实现的持续竞赛中，寻找自动发现专为量子比特硬件平台量身定制的代码和编码策略的方法正在成为一个关键问题。强化学习 （RL） 已被确定为一种很有前途的方法，但到目前为止，它在可扩展性方面受到了严重限制。在这项工作中，我们显著扩展了 RL 方法在 QEC 代码发现中的作用。显式地，我们训练了一个 RL 代理，该代理可以从头开始自动发现给定门集、量子比特连接和错误模型的 QEC 代码及其编码电路。这是通过基于 Knill-Laflamme 条件的奖励和矢量化 Clifford 模拟器实现的，该模拟器通过多达 25 个物理量子比特和距离 5 个代码展示了其有效性，同时提出了在不久的将来将这种方法扩展到 100 个量子比特和距离 10 个代码的路线图。我们还引入了噪声感知元代理的概念，它学习同时为一系列噪声模型生成编码策略，从而利用不同情况之间的见解转移。我们的方法为跨所有感兴趣的量子硬件平台加速发现 QEC 方法打开了大门。