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
Dynamical transition in controllable quantum neural networks with large depth
Bingzhi Zhang, Junyu Liu, Xiao-Chuan Wu, Liang Jiang, Quntao Zhuang
Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses
David Winderl, Nicola Franco, Jeanette Miriam Lorenz
Continuous optimization by quantum adaptive distribution search
Kohei Morimoto, Yusuke Takase, Kosuke Mitarai, Keisuke Fujii