Quantum Computing
Quantum computing aims to leverage quantum mechanics to solve problems intractable for classical computers, primarily focusing on optimization and machine learning. Current research heavily emphasizes the development and application of quantum machine learning algorithms, including variational quantum circuits, quantum neural networks, and quantum kernel methods, often integrated with classical techniques in hybrid approaches. This field holds significant potential for accelerating scientific discovery and impacting various applications, from drug discovery and materials science to financial modeling and medical diagnostics, although challenges in hardware limitations and algorithm design remain.
Papers
From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks
Andrea Ceschini, Francesco Mauro, Francesca De Falco, Alessandro Sebastianelli, Alessio Verdone, Antonello Rosato, Bertrand Le Saux, Massimo Panella, Paolo Gamba, Silvia L. Ullo
Quantum Algorithms for Compositional Text Processing
Tuomas Laakkonen, Konstantinos Meichanetzidis, Bob Coecke
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
Discrete Randomized Smoothing Meets Quantum Computing
Tom Wollschläger, Aman Saxena, Nicola Franco, Jeanette Miriam Lorenz, Stephan Günnemann
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