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
Quantum compiling with a variational instruction set for accurate and fast quantum computing
Ying Lu, Peng-Fei Zhou, Shao-Ming Fei, Shi-Ju Ran
Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning
Denis Bokhan, Alena S. Mastiukova, Aleksey S. Boev, Dmitrii N. Trubnikov, Aleksey K. Fedorov
Quantum Distributed Deep Learning Architectures: Models, Discussions, and Applications
Yunseok Kwak, Won Joon Yun, Jae Pyoung Kim, Hyunhee Cho, Minseok Choi, Soyi Jung, Joongheon Kim
A Classical-Quantum Convolutional Neural Network for Detecting Pneumonia from Chest Radiographs
Viraj Kulkarni, Sanjesh Pawale, Amit Kharat