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

TL;DR

This paper presents a scalable reinforcement learning method for automatically discovering quantum error correction codes and encoders tailored to specific hardware noise models, enabling faster development of quantum computing systems.

Contribution

It introduces a noise-aware RL framework that discovers QEC codes and encoders from scratch, scalable to 20 qubits, and capable of adapting to various noise models.

Findings

Successfully scales to 20 qubits and distance 5 codes.

Learns encoding strategies for multiple noise models.

Enables hardware-specific quantum error correction code discovery.

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 for a given gate set, qubit connectivity and error model, from scratch. This is enabled by a reward based on the Knill-Laflamme conditions and a vectorized Clifford simulator, allowing us to scale our results to 20 physical qubits and distance 5 codes. Moreover, we introduce the concept of a noise-aware meta-agent, which learns to produce encoding strategies simultaneously for a range of noise models, thus leveraging transfer of insights between different situations. Our approach opens the door towards hardware-adapted accelerated discovery of QEC approaches across the full spectrum of quantum hardware platforms of interest.