Neural Architecture Search for Quantum Autoencoders

TL;DR

This paper introduces a genetic algorithm-based neural architecture search framework to automate the design of quantum autoencoders, enhancing data compression and feature extraction in quantum machine learning.

Contribution

It presents a novel NAS method using genetic algorithms to optimize quantum autoencoder architectures, addressing design challenges in quantum circuit configuration.

Findings

Effective quantum autoencoders identified for image data

Demonstrates potential for quantum autoencoders in feature extraction

Highlights robustness of the approach in noise-prone quantum environments

Abstract

In recent years, machine learning and deep learning have driven advances in domains such as image classification, speech recognition, and anomaly detection by leveraging multi-layer neural networks to model complex data. Simultaneously, quantum computing (QC) promises to address classically intractable problems via quantum parallelism, motivating research in quantum machine learning (QML). Among QML techniques, quantum autoencoders show promise for compressing high-dimensional quantum and classical data. However, designing effective quantum circuit architectures for quantum autoencoders remains challenging due to the complexity of selecting gates, arranging circuit layers, and tuning parameters. This paper proposes a neural architecture search (NAS) framework that automates the design of quantum autoencoders using a genetic algorithm (GA). By systematically evolving variational quantum circuit (VQC) configurations, our method seeks to identify high-performing hybrid quantum-classical autoencoders for data reconstruction without becoming trapped in local minima. We demonstrate effectiveness on image datasets, highlighting the potential of quantum autoencoders for efficient feature extraction within a noise-prone, near-term quantum era. Our approach lays a foundation for broader application of genetic algorithms to quantum architecture search, aiming for a robust, automated method that can adapt to varied data and hardware constraints.