5 papers · 1 filter
Forked Physics-Informed Neural Networks for Non-Markovian Open Quantum Dynamics and Control
Zhao-Wei Wang, Kai-Yu Yuan, Feng-Hua Ren +1
Physics-informed neural networks (PINNs) provide a pathway to reunify the simulation and control of quantum systems, in which these two tasks are typically decoupled in traditional…
Forked Physics Informed Neural Networks for Coupled Systems of Differential equations
Zhao-Wei Wang, Zhao-Ming Wang
Solving coupled systems of differential equations (DEs) is a central problem across scientific computing. While Physics Informed Neural Networks (PINNs) offer a promising, mesh-fre…
Machine-Learning-Assisted Pulse Design for State Preparation in a Noisy Environment
Zhao-Wei Wang, Hong-Yang Ma, Yun-An Yan +2
High-precision quantum control is essential for quantum computing and quantum information processing. However, its practical implementation is challenged by environmental noise, wh…
Arbitrary quantum states preparation aided by deep reinforcement learning
Zhao-Wei Wang, Zhao-Ming Wang
The preparation of quantum states is essential in the realm of quantum information processing, and the development of efficient methodologies can significantly alleviate the strain…
Time series prediction of open quantum system dynamics
Zhao-Wei Wang, Zhao-Ming Wang
Time series prediction (TSP) has been widely used in various fields, such as life sciences and finance, to forecast future trends based on historical data. However, to date, there…