Quantum computing is rapidly emerging as a transformative paradigm in next‑generation intelligent systems, offering significant advantages in security, privacy, scalability, and computational efficiency. As quantum hardware, algorithms, and hybrid architectures evolve, the integration of quantum technologies with machine learning (ML) has become a central research direction with strong relevance to future multicore and many‑core systems.
This Special Session provides a focused forum for presenting advances in quantum‑enhanced ML, quantum‑inspired computation, and quantum system design, with emphasis on architectures, algorithms, and applications aligned with MCSoC’s mission.
Topics of Interest
• Quantum‑inspired machine learning and advanced ML models
Methods that borrow mathematical structures or optimization principles from quantum mechanics (e.g., tensor networks, amplitude encoding) to improve classical ML performance.
• FPGA‑based controllers and hardware acceleration for quantum computers
Design of FPGA architectures for qubit control, error correction, pulse generation, and real‑time feedback in quantum processors.
• Quantum computing architectures, systems, and platforms
System‑level design of quantum processors, qubit technologies, interconnects, cryogenic control, and integration with classical computing stacks.
• Quantum algorithms for machine‑learning and data‑driven tasks
Algorithms such as QAOA, VQE, HHL, quantum kernels, and quantum feature maps applied to classification, regression, clustering, and optimization.
• Hybrid quantum–classical learning and optimization frameworks
Variational quantum circuits, quantum neural networks, and co‑processing models where classical and quantum resources collaborate to solve ML tasks.
• Quantum learning theory and computational complexity
Theoretical foundations of quantum learnability, sample complexity, expressivity of quantum models, and separations between classical and quantum learners.
• Quantum‑enhanced robustness and generalization in ML models
Use of quantum properties (superposition, entanglement, non‑linearity) to improve model robustness, adversarial resistance, and generalization performance.
• Machine learning for experimental quantum information processing
ML techniques for qubit calibration, noise characterization, error mitigation, pulse shaping, and quantum state/process tomography.
• Fuzzy logic and soft‑computing approaches for quantum ML
Integration of fuzzy systems, uncertainty modeling, and soft‑computing paradigms with quantum algorithms or quantum‑inspired ML frameworks.
• Applications of quantum machine learning in science and engineering
Use cases in chemistry, materials science, finance, optimization, signal processing, and large‑scale data analytics where quantum ML provides measurable benefits.
