Guest Editors
Dr. Antonio Sarasa-Cabezuelo
Email: asarasa@fdi.ucm.es
Affiliation: Department of Computer Systems and Computing, Universidad Complutense de Madrid, Madrid, Spain
Homepage:
Research Interests: artificial intelligence, machine learning, medical informatics, deep learning, generative artificial intelligence
Dr. Krishna Kumar Sharma
Email: krisshna.sharma@uok.ac.in
Affiliation: Department of Computer Science & Informatics, University of Kota, Kota, India
Homepage: hl=en&oi=ao
Research Interests: data mining, big data, machine learning
Dr. Ulises Roman-Concha
Email: nromanc@unmsm.edu.pe
Affiliation: Faculty of Systems Engineering and Computer Science, Universidad Nacional Mayor de San Marcos (UNMSM), Lima, Peru
Homepage:
Research Interests: data mining, big data, machine learning
Dr. Covadonga Díez-Sanmartin
Email: mcdiez@ucm.es
Affiliation: Department of Computer Systems and Computing, Universidad Complutense de Madrid, Madrid, Spain
Homepage:
Research Interests: artificial intelligence, machine learning, feature engineering, survival analysis
Summary
Quantum computing and machine learning(ML) are emerging as complementary technologies at the forefront of computational innovation, offering new ways to tackle complex, high-dimensional problems in both science and industry. Quantum computers leverage principles such as superposition and entanglement to process information in fundamentally different ways, while machine learning techniques enable systems to learn from data and improve their performance over time. The intersection of these fields, often referred to as quantum machine learning, explores how quantum resources can accelerate learning tasks, enrich the expressiveness of models, and inspire new algorithmic paradigms.
Aim and Scope:
This special issue presents recent advances at the interface between quantum computing and machine learning, with an emphasis on both foundational work and applied research. It focuses on contributions that deepen our understanding of quantum-enhanced learning, propose new hybrid architectures, or demonstrate practical benefits in real or simulated quantum hardware. The scope includes theoretical analyses, algorithm design, software frameworks, comparative studies, and domain-oriented applications where quantum and classical learning components interact.
Suggested Themes:
· Quantum machine learning algorithms (classification, regression, generative models)
· Variational quantum circuits and hybrid quantum–classical learning
· Quantum optimization and quantum-inspired optimization for ML
· Quantum algorithms for sampling and probabilistic modeling
· Error mitigation, robustness, and reliability in quantum ML
· Benchmarks, datasets, and evaluation protocols for quantum ML
· Software tools, frameworks, and compilers for quantum ML workflows
· Quantum data, encoding strategies, and representational issues
· Security, privacy, and cryptographic aspects of quantum ML
Keywords
quantum machine learning, variational circuits, quantum kernels, NISQ algorithms, hybrid architectures, quantum optimization, error mitigation, feature encoding
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