Noise-Assisted Quantum Autoencoder

TL;DR

This paper introduces a noise-assisted quantum autoencoder that overcomes previous limitations, enabling high-fidelity compression of high-rank mixed states using measurement-informed noise channels and adaptable models for quantum annealers.

Contribution

It proposes a novel noise-assisted quantum autoencoder that improves fidelity for mixed states and introduces a measurement-informed noise setup and an adiabatic model for implementation.

Findings

Achieves high fidelity in compressing thermal states and Werner states.

Utilizes measurement results to optimize noise channels.

Extends autoencoder models to quantum annealers.

Abstract

Quantum autoencoder is an efficient variational quantum algorithm for quantum data compression. However, previous quantum autoencoders fail to compress and recover high-rank mixed states. In this work, we discuss the fundamental properties and limitations of the standard quantum autoencoder model in more depth, and provide an information-theoretic solution to its recovering fidelity. Based on this understanding, we present a noise-assisted quantum autoencoder algorithm to go beyond the limitations, our model can achieve high recovering fidelity for general input states. Appropriate noise channels are used to make the input mixedness and output mixedness consistent, the noise setup is determined by measurement results of the trash system. Compared with the original quantum autoencoder model, the measurement information is fully used in our algorithm. In addition to the circuit model, we design a (noise-assisted) adiabatic model of quantum autoencoder that can be implemented on quantum annealers. We verified the validity of our methods through compressing the thermal states of transverse field Ising model and Werner states. For pure state ensemble compression, we also introduce a projected quantum autoencoder algorithm.