Quantum autoencoders via quantum adders with genetic algorithms

TL;DR

This paper introduces a novel method for designing quantum autoencoders using optimized approximate quantum adders and genetic algorithms, enhancing resource efficiency in quantum machine learning.

Contribution

It establishes a new connection between quantum adders and autoencoders, enabling optimized autoencoder design via genetic algorithms for various quantum states.

Findings

Optimized quantum autoencoders can be implemented using quantum adders.

Genetic algorithms effectively optimize quantum autoencoders.

The approach is adaptable to controllable quantum platforms.

Abstract

The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between approximate quantum adders and quantum autoencoders. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms.