Quantum Physic
Quantum physics is currently driving innovation in machine learning and optimization, aiming to leverage quantum phenomena for computational advantages over classical approaches. Research focuses on developing and testing hybrid quantum-classical algorithms, including quantum neural networks (QNNs), variational quantum regressors (VQRs), and quantum-enhanced versions of classical algorithms like support vector machines and evolutionary algorithms, often applied to problems in image classification, medical diagnostics, and optimization tasks. These efforts are significant because they could lead to breakthroughs in fields like drug discovery, materials science, and cybersecurity by enabling faster and more efficient solutions to complex problems currently intractable for classical computers.
Papers
Q-SCALE: Quantum computing-based Sensor Calibration for Advanced Learning and Efficiency
Lorenzo Bergadano, Andrea Ceschini, Pietro Chiavassa, Edoardo Giusto, Bartolomeo Montrucchio, Massimo Panella, Antonello Rosato
Personalized Quantum Federated Learning for Privacy Image Classification
Jinjing Shi, Tian Chen, Shichao Zhang, Xuelong Li