Optimizing continuous-time quantum error correction for arbitrary noise

TL;DR

This paper introduces a machine learning-based protocol for optimizing continuous-time quantum error correction, capable of tailoring recovery strategies to complex, correlated noise processes in quantum devices.

Contribution

It develops a novel ML-driven method to optimize both code space and recovery maps for arbitrary noise in continuous-time quantum error correction.

Findings

Successfully identifies optimal recovery strategies for complex noise

Enhances fidelity of logical quantum states

Adapts to device-specific noise characteristics

Abstract

We present a protocol using machine learning (ML) to simultaneously optimize the quantum error-correcting code space and the corresponding recovery map in the framework of continuous-time quantum error correction. Given a Hilbert space and a noise process -- potentially correlated across both space and time -- the protocol identifies the optimal recovery strategy, measured by the average logical state fidelity. This approach enables the discovery of recovery schemes tailored to arbitrary device-level noise.