Quantum autoencoders with enhanced data encoding

TL;DR

The paper introduces EF-QAE, a variational quantum autoencoder that enhances quantum data compression fidelity by incorporating feature vectors, demonstrated on Ising model states and handwritten digits, suitable for near-term quantum devices.

Contribution

It proposes a novel feature-based variational quantum autoencoder that improves compression fidelity over standard methods.

Findings

EF-QAE outperforms standard autoencoders in simulations.

The method achieves higher fidelity with the same quantum resources.

Classical optimization enhances the autoencoder's performance.

Abstract

We present the enhanced feature quantum autoencoder, or EF-QAE, a variational quantum algorithm capable of compressing quantum states of different models with higher fidelity. The key idea of the algorithm is to define a parameterized quantum circuit that depends upon adjustable parameters and a feature vector that characterizes such a model. We assess the validity of the method in simulations by compressing ground states of the Ising model and classical handwritten digits. The results show that EF-QAE improves the performance compared to the standard quantum autoencoder using the same amount of quantum resources, but at the expense of additional classical optimization. Therefore, EF-QAE makes the task of compressing quantum information better suited to be implemented in near-term quantum devices.