Decoding surface codes with deep reinforcement learning and probabilistic policy reuse

Quantum computing (QC) promises significant advantages on certain hard computational tasks over classical computers. However, existing quantum hardware, the so-called noisy intermediate-scale quantum computers (NISQ), are still unable to carry out computations faithfully mainly because of the lack of quantum error correction (QEC) capability.

A significant amount of theoretical studies have provided various types of QEC codes; one of the notable topological codes is the surface code. Recent developments of ML-based techniques especially the reinforcement learning (RL) methods are trying to tackle this challenge and have already made certain progress. Nevertheless, the device noise pattern may change over time, making trained decoder models ineffective. In this work, we propose a continual reinforcement learning method to tackle these decoding challenges. Specifically, we construct a double deep Q-learning with probabilistic policy reuse (DDQN-PPR) model to learn surface code decoding strategies of quantum environments with varying noise patterns.