A Symplectic Based Neural Network Algorithm for Quantum Controls under Uncertainty

DOI10.4208/cicp.OA-2021-0219zbMath1490.81087WikidataQ114911794 ScholiaQ114911794MaRDI QIDQ5077710

Song Chen, Yanzhao Cao, Lijin Wang, Jingshi Li

Publication date: 19 May 2022

Published in: Communications in Computational Physics (Search for Journal in Brave)

zbMATH Keywords

neural networksymplectic methodsnorm-preservationquantum (noise) control

Mathematics Subject Classification ID

Closed and approximate solutions to the Schrödinger, Dirac, Klein-Gordon and other equations of quantum mechanics (81Q05) Numerical methods for Hamiltonian systems including symplectic integrators (65P10) Discrete approximations in optimal control (49M25) Quantum control (81Q93)

Uses Software

• DiffSharp
• torchdiffeq
• ANODEs

Cites Work

• Unnamed Item
• Symplectic neural networks in Taylor series form for Hamiltonian systems
• Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
• A proposal on machine learning via dynamical systems
• Quantum Computation and Quantum Information
• Solving high-dimensional partial differential equations using deep learning
• Ensemble Control of Bloch Equations
• Control of quantum phenomena: past, present and future