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
Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization
Georg Kruse, Rodrigo Coehlo, Andreas Rosskopf, Robert Wille, Jeanette Miriam Lorenz
Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcare
Amandeep Singh Bhatia, David E. Bernal Neira