A Hybrid Quantum-Classical Physics-Informed Neural Network Architecture for Solving Quantum Optimal Control Problems

TL;DR

This paper introduces a hybrid quantum-classical PINN framework utilizing dynamic quantum circuits to solve quantum optimal control problems, demonstrating its effectiveness on multi-level quantum systems.

Contribution

It presents a novel integrated quantum-classical neural network architecture combining quantum circuits with PINNs for quantum control tasks.

Findings

Successfully applied to two and three-level quantum systems

Demonstrated improved control optimization performance

Showcased potential for broader quantum computing applications

Abstract

This paper proposes an integrated quantum-classical approach that merges quantum computational dynamics with classical computing methodologies tailored to address control problems based on Pontryagin's minimum principle within a Physics-Informed Neural Network (PINN) framework. By leveraging a dynamic quantum circuit that combines Gaussian and non-Gaussian gates, the study showcases an innovative approach to optimizing quantum state manipulations. The proposed hybrid model effectively applies machine learning techniques to solve optimal control problems. This is illustrated through the design and implementation of a hybrid PINN network to solve a quantum state transition problem in a two and three-level system, highlighting its potential across various quantum computing applications.