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
Trained quantum neural networks are Gaussian processes
Filippo Girardi, Giacomo De Palma
Quantum Computing-Enhanced Algorithm Unveils Novel Inhibitors for KRAS
Mohammad Ghazi Vakili, Christoph Gorgulla, AkshatKumar Nigam, Dmitry Bezrukov, Daniel Varoli, Alex Aliper, Daniil Polykovsky, Krishna M. Padmanabha Das, Jamie Snider, Anna Lyakisheva, Ardalan Hosseini Mansob, Zhong Yao, Lela Bitar, Eugene Radchenko, Xiao Ding, Jinxin Liu, Fanye Meng, Feng Ren, Yudong Cao, Igor Stagljar, Alán Aspuru-Guzik, Alex Zhavoronkov