Learning Better Error Correction Codes with Hybrid Quantum-Assisted Machine Learning

TL;DR

This paper introduces a hybrid classical-quantum machine learning approach to optimize quantum error correction codes, leveraging quantum devices to improve code design for specific errors in quantum computing.

Contribution

It presents a novel hybrid algorithm combining classical reinforcement learning with quantum device calls to enhance stabilizer code construction.

Findings

Improved error correcting codes tailored to specific device errors

Effective integration of quantum devices in the code search process

Demonstrated potential for hybrid approaches in quantum error correction

Abstract

Quantum error correction is one of the fundamental building blocks of digital quantum computation. The Quantum Lego formalism has introduced a systematic way of constructing new stabilizer codes out of basic lego-like building blocks, which in previous work we have used to generate improved error correcting codes via an automated reinforcement learning process. Here, we take this a step further and show the use of a hybrid classical-quantum algorithm. We combine classical reinforcement learning with calls to two commercial quantum devices to search for a stabilizer code to correct errors specific to the device, as well as an induced photon loss error.