Quantum autoencoders to denoise quantum data

TL;DR

This paper introduces a quantum autoencoder that effectively denoises entangled quantum states, such as GHZ states, which are crucial for quantum technologies, by using unsupervised learning techniques.

Contribution

The paper presents a novel quantum autoencoder capable of denoising entangled states under realistic noise conditions, advancing quantum neural network applications.

Findings

Successfully denoised GHZ states with spin-flip errors

Effective in removing random unitary noise

Potential to enhance quantum technology robustness

Abstract

Entangled states are an important resource for quantum computation, communication, metrology, and the simulation of many-body systems. However, noise limits the experimental preparation of such states. Classical data can be efficiently denoised by autoencoders---neural networks trained in unsupervised manner. We develop a novel quantum autoencoder that successfully denoises Greenberger-Horne-Zeilinger states subject to spin-flip errors and random unitary noise. Various emergent quantum technologies could benefit from the proposed unsupervised quantum neural networks.