Physics-informed neural networks for quantum control

TL;DR

This paper introduces physics-informed neural networks (PINNs) as an efficient computational method for solving optimal quantum control problems, demonstrating high performance and flexibility in open quantum systems.

Contribution

The paper presents a novel application of PINNs to quantum control, showing advantages over traditional methods in efficiency, adaptability, and energy consumption.

Findings

High-probability state-to-state transfer achieved

Short-time evolution with low-energy controls demonstrated

PINNs outperform standard control techniques in flexibility

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.