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
Quantum Machine Learning Architecture Search via Deep Reinforcement Learning
Xin Dai, Tzu-Chieh Wei, Shinjae Yoo, Samuel Yen-Chi Chen
Quantum Computing and Neuromorphic Computing for Safe, Reliable, and explainable Multi-Agent Reinforcement Learning: Optimal Control in Autonomous Robotics
Mazyar Taghavi
Quantum Long Short-Term Memory for Drug Discovery
Liang Zhang, Yin Xu, Mohan Wu, Liang Wang, Hua Xu