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
GroverGPT: A Large Language Model with 8 Billion Parameters for Quantum Searching
Haoran Wang, Pingzhi Li, Min Chen, Jinglei Cheng, Junyu Liu, Tianlong Chen
Quantum Diffusion Model for Quark and Gluon Jet Generation
Mariia Baidachna, Rey Guadarrama, Gopal Ramesh Dahale, Tom Magorsch, Isabel Pedraza, Konstantin T. Matchev, Katia Matcheva, Kyoungchul Kong, Sergei Gleyzer
Mitigating exponential concentration in covariant quantum kernels for subspace and real-world data
Gabriele Agliardi, Giorgio Cortiana, Anton Dekusar, Kumar Ghosh, Naeimeh Mohseni, Corey O'Meara, Víctor Valls, Kavitha Yogaraj, Sergiy Zhuk
Quantum vs. Classical Machine Learning Algorithms for Software Defect Prediction: Challenges and Opportunities
Md Nadim, Mohammad Hassan, Ashis Kumar Mandal, Chanchal K. Roy