Simultaneous Discovery of Quantum Error Correction Codes and Encoders with a Noise-Aware Reinforcement Learning Agent

Understand

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 for a given gate set, qubit connectivity and error model, from scratch.