Scaling Automated Quantum Error Correction Discovery with Reinforcement Learning

Abstract

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 from scratch. We show its effectiveness with up to 25 physical qubits and distance 5 codes, while presenting a roadmap to scale up to 100 qubits and distance 10 codes in the near future.