Neural Architecture Search for Quantum Autoencoders

2025-11-24

AI Summary

Quantum autoencoder architectures suffer from design complexity and susceptibility to local optima. Method: This paper proposes a genetic-algorithm-based neural architecture search (NAS) framework that jointly optimizes quantum circuit topology, gate types, layer ordering, and variational parameters. It employs a classical-quantum hybrid training paradigm to perform end-to-end search over variational quantum circuits for high-performance autoencoders. Contribution/Results: Experiments on image datasets demonstrate that the automatically discovered lightweight quantum circuits achieve efficient high-dimensional data compression and high-fidelity reconstruction. The resulting models exhibit enhanced generalization capability and improved compatibility with noisy intermediate-scale quantum (NISQ) devices. Crucially, they enable robust feature extraction under realistic hardware noise, validating both feasibility and practical utility for near-term quantum machine learning applications.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Automating quantum autoencoder design to overcome manual circuit architecture challenges
• Optimizing variational quantum circuits for efficient data compression and reconstruction
• Developing noise-resilient quantum autoencoders for near-term imperfect hardware

Innovation

• Genetic algorithm automates quantum autoencoder circuit design
• Evolves variational quantum circuit configurations systematically
• Hybrid quantum-classical autoencoders for efficient feature extraction