Physics-Informed Neural Networks for Quantum Control

2022/06/13 by Ariel Norambuena, Norambuena, Ariel, Marios Mattheakis +6 · 59 citations
Computer Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Computer science #Field (mathematics) #Flexibility (engineering) #Mathematical optimization #Mathematics #Neural Networks and Reservoir Computing #Optimal control #Physics #Quantum #Quantum Information and Cryptography #Quantum computer #Quantum dynamics #Quantum mechanics #Spectroscopy and Quantum Chemical Studies #Statistical physics

paper · pdf · doi:10.1103/physrevlett.132.010801

published in Physical Review Letters 132(1), 010801 (American Physical Society)

openalex publication_date 2024/01/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

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.

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• Modeling Terahertz Propagation via Frequency-Domain Physics-Informed Neural Networks
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• Neural Network Solution of Non-Markovian Quantum State Diffusion and Operator Construction of Quantum Stochastic Process
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• Machine learning meets su(n) Lie algebra: Enhancing quantum dynamics learning with exact trace conservation
• Neural network approach to mitigating intra-gate crosstalk in superconducting CZ gates
• Time-of-Flow Distributions in Discrete Quantum Systems: From Operational Protocols to Quantum Speed Limits

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