Quantum Machine Learning: Algorithms, Architectures, and Real-World Applications

Quantum machine learning (QML) sits at one of the most contested and productive intersections in contemporary physics. Variational algorithms are moving from theoretical proposals to experimental validation on real hardware; the debate over where quantum advantage first emerges in learning tasks is drawing researchers from both sides of the quantum–AI divide; and hybrid architectures are opening ground that neither field could reach alone. The research produced in the next few years will shape how these communities converge.This collection focuses on work that moves the field from theoretical promise toward practical, hardware-constrained impact. We are particularly interested in contributions that engage with real-world datasets, empirical constraints, and rigorous benchmarking against strong classical baselines.The collection welcomes original research, reviews, and perspectives across the following themes:• Hardware-Aware QML & NISQ-Era Constraints — noise-aware protocols, circuit depth optimisation, decoherence mitigation, and implementation under limited qubit counts on near-term devices.• Variational & Quantum-Enhanced Training — variational quantum eigensolvers, parameterised circuits, optimisation landscapes, and trainability under realistic noise.• Error Mitigation for Learning Tasks — noise-aware training strategies, error mitigation protocols tailored to ML workloads, and robustness under realistic hardware constraints.• Hybrid Quantum–Classical Frameworks — integration strategies with classical ML pipelines, co-design approaches, and deployment on embedded and edge-AI platforms.• Resource-Efficient QML & Data Encoding — architectures designed for near-term devices; feature-map design for spectroscopic, chemical, imaging, and biological datasets.• Applications in Chemistry, Materials Science & AI-Driven Quantum Control — quantum-assisted molecular simulation, materials discovery, property prediction, and AI-driven optimisation of quantum experiments and control protocols.• Quantum-Enhanced Sensing & Biosensing — signal classification, noise reduction, and feature extraction supporting healthcare, diagnostic, and point-of-care applications.• Benchmarking & Quantum Advantage — standardised benchmarking protocols, complexity-theoretic bounds, and empirical validation against strong classical baselines.• Reproducibility & Standard Datasets — community benchmarks, open datasets, and protocols for fair comparison across QML methods.• Translational Barriers & Commercialisation — pathways from research to deployment in life sciences, sensing, and medical diagnostics; regulatory and scalability considerations.Article Types:The collection is open to Original Research, Review, Mini-Review, Perspective, and Methods articles. Cross-listed contributions from related Frontiers sections are welcome where the scope aligns. Topic editors: Kelvin Mpofu, C.-C. (Joseph) Wang, R. Gökhan Türeci Source: https://www.frontiersin.org/research-topics/84193/quantum-machine-learning-algorithms-architectures-and-real-world-applications