Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation
This work addresses the critical need for efficient decoders in quantum computing, offering a novel approach that could enhance scalability, though it appears incremental as it applies existing RL methods to a known bottleneck.
The authors tackled the problem of decoding topological error-correcting codes for fault-tolerant quantum computation by reformulating it as a reinforcement learning task, resulting in fast decoding agents for the surface code across various noise models.
Topological error correcting codes, and particularly the surface code, currently provide the most feasible roadmap towards large-scale fault-tolerant quantum computation. As such, obtaining fast and flexible decoding algorithms for these codes, within the experimentally relevant context of faulty syndrome measurements, is of critical importance. In this work, we show that the problem of decoding such codes, in the full fault-tolerant setting, 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. As a demonstration, by using deepQ learning, we obtain fast decoding agents for the surface code, for a variety of noise-models.