Quantum Error Correction with Quantum Autoencoders

TL;DR

This paper explores how quantum autoencoders, a form of quantum neural networks, can be trained to perform active quantum error correction, including error detection, correction, and discovering new logical encodings, even under noisy conditions.

Contribution

It demonstrates the use of quantum autoencoders for active error correction, error detection, and discovering noise-adapted logical encodings in quantum memory.

Findings

Quantum autoencoders can learn optimal error correction strategies.

They can protect entire logical codespaces, not just specific states.

Autoencoders remain effective even with moderate noise.

Abstract

Active quantum error correction is a central ingredient to achieve robust quantum processors. In this paper we investigate the potential of quantum machine learning for quantum error correction in a quantum memory. Specifically, we demonstrate how quantum neural networks, in the form of quantum autoencoders, can be trained to learn optimal strategies for active detection and correction of errors, including spatially correlated computational errors as well as qubit losses. We highlight that the denoising capabilities of quantum autoencoders are not limited to the protection of specific states but extend to the entire logical codespace. We also show that quantum neural networks can be used to discover new logical encodings that are optimally adapted to the underlying noise. Moreover, we find that, even in the presence of moderate noise in the quantum autoencoders themselves, they may still be successfully used to perform beneficial quantum error correction and thereby extend the lifetime of a logical qubit.