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
A quantum moving target segmentation algorithm for grayscale video
Wenjie Liu, Lu Wang, Qingshan Wu
Quantum generative adversarial learning in photonics
Yizhi Wang, Shichuan Xue, Yaxuan Wang, Yong Liu, Jiangfang Ding, Weixu Shi, Dongyang Wang, Yingwen Liu, Xiang Fu, Guangyao Huang, Anqi Huang, Mingtang Deng, Junjie Wu