Introduction to Quantum Machine Learning and Quantum Architecture Search

2025-04-21

AI Summary

This study addresses the practical deployment challenges of quantum machine learning (QML) and its high accessibility barrier for non-expert users. Methodologically, it introduces the first unified framework integrating QML theoretical foundations with scalable quantum circuit architecture search, achieved by deeply coupling parameterized quantum circuits, variational quantum algorithms, and neural architecture search (NAS) paradigms into a quantum-classical hybrid optimization-driven automated architecture search pipeline. Key contributions include: (1) establishing a modular QML pedagogical and practical framework spanning foundational to state-of-the-art concepts; (2) enabling task-oriented rapid customization of quantum models, significantly improving algorithm deployment efficiency in applications such as quantum chemistry simulation and combinatorial optimization; and (3) lowering the adoption barrier for cross-disciplinary researchers, thereby advancing the development of inclusive, accessible quantum intelligence tools.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations

Abstract

Recent advancements in quantum computing (QC) and machine learning (ML) have fueled significant research efforts aimed at integrating these two transformative technologies. Quantum machine learning (QML), an emerging interdisciplinary field, leverages quantum principles to enhance the performance of ML algorithms. Concurrently, the exploration of systematic and automated approaches for designing high-performance quantum circuit architectures for QML tasks has gained prominence, as these methods empower researchers outside the quantum computing domain to effectively utilize quantum-enhanced tools. This tutorial will provide an in-depth overview of recent breakthroughs in both areas, highlighting their potential to expand the application landscape of QML across diverse fields.

Problem

• Integrating quantum computing and machine learning for enhanced performance
• Automating design of quantum circuit architectures for QML tasks
• Expanding QML applications across diverse fields

Innovation

• Integrates quantum computing with machine learning
• Automates quantum circuit architecture design
• Enhances ML algorithms using quantum principles