Information loss and run time from practical application of quantum data compression

TL;DR

This paper explores the practical aspects of quantum data compression, including information loss, resource costs, and run times, demonstrating near-lossless compression of image data using hybrid quantum autoencoders on IBM quantum hardware.

Contribution

It provides a detailed implementation of a hybrid quantum autoencoder algorithm for data compression, including real-device experiments and analysis of operational conditions.

Findings

Near-lossless compression achieved in simulation

Successful implementation on IBM quantum hardware

Analysis of run times and conditions for quantum compression

Abstract

We examine information loss, resource costs, and run time from practical application of quantum data compression. Compressing quantum data to fewer qubits enables efficient use of resources, as well as applications for quantum communication and denoising. In this context, we provide a description of the quantum and classical components of the hybrid quantum autoencoder algorithm, implemented using IBM's Qiskit language. Utilizing our own data sets, we encode bitmap images as quantum superposition states, which correspond to linearly independent vectors with density matrices of discrete values. We successfully compress this data with near-lossless compression using simulation, and then run our algorithm on an IBMQ quantum chip. We describe conditions and run times for compressing our data on quantum devices.