Reinforcement Learning Decoders for Fault-Tolerant Quantum Computation

TL;DR

This paper introduces reinforcement learning-based decoders for fault-tolerant quantum computation, demonstrating fast and adaptable decoding agents for surface codes under various noise conditions.

Contribution

It reformulates the decoding problem as a reinforcement learning task and applies deepQ learning to develop efficient decoders for the surface code.

Findings

DeepQ learning produces fast decoding agents

Decoders work across multiple noise models

Reinforcement learning offers a flexible approach

Abstract

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.