Submission Deadline: 30 September 2026 View: 2643 Submit to Special Issue
Prof. Francisco Martínez Álvarez
Email: fmaralv@upo.es
Affiliation: Data Science and Big Data Lab, Universidad Pablo de Olavide, Seville, Spain
Research Interests: quantum computing, machine learning, time series
Assoc. Prof. David Gutiérrez-Avilés
Email: dgutierrez3@us.es
Affiliation: Department of Computer Languages and Systems, University of Seville, Seville, Spain
Research Interests: quantum computing, machine learning, evolutionary computing
Assoc. Prof. Richard Jiang
Email: r.jiang2@lancaster.ac.uk
Affiliation: School of Computing and Commutations, Lancaster University, Lancaster, UK
Research Interests: quantum computing, machine learning, pattern recognition
Quantum Machine Learning has emerged as a rapidly evolving interdisciplinary field at the intersection of quantum computing, artificial intelligence, and statistical learning theory. Recent progress in quantum hardware, quantum algorithms, and hybrid quantum classical architectures has renewed interest in understanding how quantum resources can enhance learning, optimization, and data driven modeling.
This Special Issue aims to provide a timely forum for presenting recent theoretical developments, algorithmic advances, and practical applications in Quantum Machine Learning. The issue seeks to capture both foundational insights and applied research that demonstrate the current state of the art, as well as future directions of this fast-growing field.
In line with current best practices in reproducible research, submissions that provide openly available code, data, and experimental workflows are particularly encouraged. Contributions that include executions on real quantum hardware, rather than simulations only, are especially welcome, as they provide valuable understanding into the practical capabilities and limitations of near term quantum devices.
Topics of interest include, but are not limited to, the following areas:
· Quantum learning theory and complexity analysis
· Variational quantum algorithms for machine learning
· Hybrid quantum-classical learning models
· Quantum neural networks and quantum circuits for learning
· Quantum kernel methods and feature maps
· Optimization methods for quantum machine learning
· Noise, robustness, and error mitigation in learning models
· Learning with near-term quantum devices
· Applications of quantum machine learning in engineering, physics, and data science
· Software frameworks and tools for quantum machine learning
· Comparisons between classical and quantum learning approaches
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