Quantum autoencoders for efficient compression of quantum data

TL;DR

This paper introduces quantum autoencoders that learn to efficiently compress quantum data, enabling reduced quantum resource usage for storing and processing quantum states, with applications in quantum simulation.

Contribution

It presents the concept and implementation of quantum autoencoders trained via classical algorithms for quantum data compression, a novel approach in quantum information processing.

Findings

Successfully trained a quantum autoencoder for specific quantum states

Demonstrated compression of ground states in quantum simulation tasks

Showed potential for resource-efficient quantum data storage

Abstract

Classical autoencoders are neural networks that can learn efficient low dimensional representations of data in higher dimensional space. The task of an autoencoder is, given an input $x$, is to map $x$ to a lower dimensional point $y$ such that $x$ can likely be recovered from $y$. The structure of the underlying autoencoder network can be chosen to represent the data on a smaller dimension, effectively compressing the input. Inspired by this idea, we introduce the model of a quantum autoencoder to perform similar tasks on quantum data. The quantum autoencoder is trained to compress a particular dataset of quantum states, where a classical compression algorithm cannot be employed. The parameters of the quantum autoencoder are trained using classical optimization algorithms. We show an example of a simple programmable circuit that can be trained as an efficient autoencoder. We apply our model in the context of quantum simulation to compress ground states of the Hubbard model and molecular Hamiltonians.