Beyond Classical Limits: Quantum Machine Learning for Multi-Field Research
A special issue of Quantum Reports (ISSN 2624-960X).
Deadline for manuscript submissions: 31 December 2025 | Viewed by 638
Special Issue Editors
2. Portsmouth Artificial Intelligence and Data Science Centre, University of Portsmouth, Winston Churchill Ave., Southsea, Portsmouth PO1 2UP, UK
Interests: quantum computing; machine learning; deep learning; quantum machine learning applications
Interests: quantum computation; quantum communication; simulation of complex quantum systems; high-precision sensing and imaging
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of Quantum Machine Learning (QML), the fusion of quantum computing and machine learning, offers unprecedented opportunities to advance research across multiple scientific fields. Quantum-enhanced algorithms provide powerful tools for overcoming the limitations of classical methods, enabling researchers to tackle complex problems with greater accuracy, efficiency, and speed.
Recent breakthroughs in quantum simulators, variational quantum algorithms, and quantum generative models have made it possible to apply quantum computing to a range of challenging tasks, such as predictive modelling, optimization, and large-scale data analysis. As access to quantum hardware expands, the potential for QML to revolutionize diverse sectors, including but not limited to materials science, environmental monitoring, healthcare, finance, and aerospace, is increasingly becoming tangible.
This Special Issue invites original contributions, technical papers, simulations, and reviews that explore the theoretical foundations, practical applications, and forward-looking perspectives of QML and quantum generative models across various interdisciplinary research areas. While the primary focus will be on the applications of QML in these fields, the scope of this Special Issue is not limited to the defined fields and welcomes contributions from all areas where QML can have a transformative impact.
We aim to highlight how quantum computing can transcend classical limitations and accelerate breakthroughs across a wide range of scientific domains, fostering innovation and expanding the boundaries of what is possible in research and technology.
Dr. Fahad Ahmad
Prof. Dr. Vincenzo Tamma
Guest Editor
Manuscript Submission Information
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Keywords
- quantum machine learning
- quantum generative models
- quantum neural networks
- hybrid quantum–classical algorithms
- quantum optimization
- predictive modelling and optimization using QML
- QML in materials science and energy storage
- quantum computing in environmental monitoring
- quantum algorithms for healthcare and medical diagnostics
- financial modelling and risk analysis with QML
- quantum-enhanced applications in aerospace and engineering
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