Scaling Automated Quantum Error Correction Discovery with Reinforcement Learning

Jan Olle, Remmy Zen, Matteo Puviani, Florian Marquardt

Keywords: reinforcement learning, quantum error correction, quantum physics, proximal policy optimization

TL;DR: We present a scalable framework based on reinforcement learning to codiscover quantum error correction codes and their encoding circuits

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.

Submission Number: 56