Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning

TL;DR

This paper reports the experimental implementation of a quantum autoencoder that compresses qutrits into qubits using machine learning, achieving low-error, lossless compression with minimal prior data and robustness to perturbations.

Contribution

It is the first experimental realization of a quantum autoencoder that efficiently compresses quantum data with minimal prior information and robustness.

Findings

Successfully compressed qutrits to qubits with low error

Achieved lossless compression when data structure permits

Device is robust to perturbations during optimization

Abstract

With quantum resources a precious commodity, their efficient use is highly desirable. Quantum autoencoders have been proposed as a way to reduce quantum memory requirements. Generally, an autoencoder is a device that uses machine learning to compress inputs, that is, to represent the input data in a lower-dimensional space. Here, we experimentally realize a quantum autoencoder, which learns how to compress quantum data using a classical optimization routine. We demonstrate that when the inherent structure of the data set allows lossless compression, our autoencoder reduces qutrits to qubits with low error levels. We also show that the device is able to perform with minimal prior information about the quantum data or physical system and is robust to perturbations during its optimization routine.