Quantum Machine Learning for Complex Systems

• URL: http://arxiv.org/abs/2602.20352v1
• Date: Mon, 23 Feb 2026 20:54:08 GMT
• Title: Quantum Machine Learning for Complex Systems
• Authors: Vinit Singh, Amandeep Singh Bhatia, Mandeep Kaur Saggi, Manas Sajjan, Sabre Kais,
• Abstract summary: Quantum machine learning (QML) is rapidly transitioning from theoretical promise to practical relevance.<n>This Review provides a structured overview of advances that bridge foundational quantum learning principles with real-world applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum machine learning (QML) is rapidly transitioning from theoretical promise to practical relevance across data-intensive scientific domains. In this Review, we provide a structured overview of recent advances that bridge foundational quantum learning principles with real-world applications. We survey foundational QML paradigms, including variational quantum algorithms, quantum kernel methods, and neural-network quantum states, with emphasis on their applicability to complex quantum systems. We examine neural-network quantum states as expressive variational models for correlated matter, non-equilibrium dynamics, and open quantum systems, and discuss fundamental challenges associated with training and sampling. Recent advances in quantum-enhanced sampling and diagnostics of learning dynamics, including information-theoretic tools, are reviewed as mechanisms for improving scalability and trainability. The Review further highlights application-driven QML frameworks in drug discovery, cancer biology, and agro-climate modeling, where data complexity and constraints motivate hybrid quantum-classical approaches. We conclude with a discussion of federated quantum machine learning as a route to distributed, privacy-preserving quantum intelligence. Overall, this Review presents a unified perspective on the opportunities and limitations of QML for complex systems.

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