Physics-informed neural networks for quantum control

Abstract

Quantum control is a ubiquitous research field that has enabled physicists to delve into the dynamics and features of quantum systems, delivering powerful applications for various atomic, optical, mechanical, and solid-state systems. In recent years, traditional control techniques based on optimization processes have been translated into efficient artificial intelligence algorithms. Here, we introduce a computational method for optimal quantum control problems via physics-informed neural networks (PINNs). We apply our methodology to open quantum systems by efficiently solving the state-to-state transfer problem with high probabilities, short-time evolution, and using low-energy consumption controls. Furthermore, we illustrate the flexibility of PINNs to solve the same problem under changes in physical parameters and initial conditions, showing advantages in comparison with standard control techniques.

arXiv comment17 pages, 13 figures, 2 tables

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• TorchQC -- A framework for efficiently integrating machine and deep learning methods in quantum dynamics and control
• Fast generation of entanglement between coupled spins using optimization and deep learning methods
• Physical Neural Networks with Self-Learning Capabilities
• Neural Quantum Propagators for Driven-Dissipative Quantum Dynamics
• Deep-learning design of graphene metasurfaces for quantum control and Dirac electron holography
• Twin-Space Representation of Classical Mapping Model in the Constraint Phase Space Representation: Numerically Exact Approach to Open Quantum Systems
• Time-of-Flow Distributions in Discrete Quantum Systems: From Operational Protocols to Quantum Speed Limits
• Machine learning meets $\mathfrak{su}(n)$ Lie algebra: Enhancing quantum dynamics learning with exact trace conservation
• Physics-Informed Neural Network for Elastic Wave-Mode Separation
• Control of Overfitting with Physics
• Highly efficient nuclear population transfer through physics-informed neural networks
• AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes
• Dipolar optimal control of entangled current states
• Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations