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 Annealing-Based Algorithm for Efficient Coalition Formation Among LEO Satellites
Supreeth Mysore Venkatesh, Antonio Macaluso, Marlon Nuske, Matthias Klusch, Andreas Dengel
Quantum Gradient Class Activation Map for Model Interpretability
Hsin-Yi Lin, Huan-Hsin Tseng, Samuel Yen-Chi Chen, Shinjae Yoo
Quantum Machine Learning: Performance and Security Implications in Real-World Applications
Zhengping Jay Luo, Tyler Stewart, Mourya Narasareddygari, Rui Duan, Shangqing Zhao
Pediatric TSC-Related Epilepsy Classification from Clinical MR Images Using Quantum Neural Network
Ling Lin, Yihang Zhou, Zhanqi Hu, Dian Jiang, Congcong Liu, Shuo Zhou, Yanjie Zhu, Jianxiang Liao, Dong Liang, Hairong Zheng, Haifeng Wang
Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models
Shailendra Bhandari, Pedro Lincastre, Pedro Lind
Analyzing the Effectiveness of Quantum Annealing with Meta-Learning
Riccardo Pellini, Maurizio Ferrari Dacrema
How quantum and evolutionary algorithms can help each other: two examples
Shailendra Bhandari, Stefano Nichele, Sergiy Denysov, Pedro G. Lind