{"id":5448,"date":"2024-02-11T07:15:35","date_gmt":"2024-02-11T07:15:35","guid":{"rendered":"https:\/\/mcsoc-forum.org\/site\/?page_id=5448"},"modified":"2026-03-11T02:13:10","modified_gmt":"2026-03-11T02:13:10","slug":"quantum-technology-and-machine-learning","status":"publish","type":"page","link":"https:\/\/mcsoc-forum.org\/site\/index.php\/quantum-technology-and-machine-learning\/","title":{"rendered":"Special Session on Quantum Technology and Machine Learning"},"content":{"rendered":"\n
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Session Chair<\/h2>\n
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\"Prof.<\/figure>

Prof. Deepika Saxena<\/a><\/strong>
The University of Aizu, Japan<\/span><\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

Quantum computing is rapidly emerging as a transformative paradigm in next\u2011generation intelligent systems, offering significant advantages in security<\/strong>, privacy<\/strong>, scalability, and computational efficiency. As quantum hardware, algorithms, and hybrid architectures evolve, the integration of quantum technologies<\/strong> with machine learning (ML)<\/strong> has become a central research direction with strong relevance to future multicore and many\u2011core systems.<\/p>\n\n\n\n

This Special Session provides a focused forum for presenting advances in quantum\u2011enhanced ML<\/strong>, quantum\u2011inspired computation<\/strong>, and quantum system design<\/strong>, with emphasis on architectures, algorithms, and applications aligned with MCSoC\u2019s mission. <\/p>\n\n\n\n

Topics of Interest<\/p>\n\n\n\n

\u2022 Quantum\u2011inspired machine learning and advanced ML models<\/strong><\/h4>\n\n\n\n

Methods that borrow mathematical structures or optimization principles from quantum mechanics (e.g., tensor networks, amplitude encoding) to improve classical ML performance.<\/p>\n\n\n\n

\u2022 FPGA\u2011based controllers and hardware acceleration for quantum computers<\/strong><\/h4>\n\n\n\n

Design of FPGA architectures for qubit control, error correction, pulse generation, and real\u2011time feedback in quantum processors.<\/p>\n\n\n\n

\u2022 Quantum computing architectures, systems, and platforms<\/strong><\/h4>\n\n\n\n

System\u2011level design of quantum processors, qubit technologies, interconnects, cryogenic control, and integration with classical computing stacks.<\/p>\n\n\n\n

\u2022 Quantum algorithms for machine\u2011learning and data\u2011driven tasks<\/strong><\/h4>\n\n\n\n

Algorithms such as QAOA, VQE, HHL, quantum kernels, and quantum feature maps applied to classification, regression, clustering, and optimization.<\/p>\n\n\n\n

\u2022 Hybrid quantum\u2013classical learning and optimization frameworks<\/strong><\/h4>\n\n\n\n

Variational quantum circuits, quantum neural networks, and co\u2011processing models where classical and quantum resources collaborate to solve ML tasks.<\/p>\n\n\n\n

\u2022 Quantum learning theory and computational complexity<\/strong><\/h4>\n\n\n\n

Theoretical foundations of quantum learnability, sample complexity, expressivity of quantum models, and separations between classical and quantum learners.<\/p>\n\n\n\n

\u2022 Quantum\u2011enhanced robustness and generalization in ML models<\/strong><\/h4>\n\n\n\n

Use of quantum properties (superposition, entanglement, non\u2011linearity) to improve model robustness, adversarial resistance, and generalization performance.<\/p>\n\n\n\n

\u2022 Machine learning for experimental quantum information processing<\/strong><\/h4>\n\n\n\n

ML techniques for qubit calibration, noise characterization, error mitigation, pulse shaping, and quantum state\/process tomography.<\/p>\n\n\n\n

\u2022 Fuzzy logic and soft\u2011computing approaches for quantum ML<\/strong><\/h4>\n\n\n\n

Integration of fuzzy systems, uncertainty modeling, and soft\u2011computing paradigms with quantum algorithms or quantum\u2011inspired ML frameworks.<\/p>\n\n\n\n

\u2022 Applications of quantum machine learning in science and engineering<\/strong><\/h4>\n\n\n\n

Use cases in chemistry, materials science, finance, optimization, signal processing, and large\u2011scale data analytics where quantum ML provides measurable benefits.<\/p>\n\n\n\n

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\n PATRON, HOST, and SPONSORS\n <\/h3>\n\n \n
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