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

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Dehaghani, NB (Author) Other; Aguiar, AP (Author) FEUP; Wisniewski, R (Author) Other

Conference proceedings International

Title: IEEE International Conference on Quantum Computing and Engineering, QCE 2024, Montreal, QC, Canada, September 15-20, 2024

Pages: 1378-1386

5th IEEE International Conference on Quantum Computing and Engineering, QCE 2024

Montreal, 15 September 2024 through 20 September 2024

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-017-YJW

DOI: 10.1109/qce60285.2024.00164

Abstract (EN): 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 lever-aging 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 structure to solve a quantum state transition problem in a two and three-level system, highlighting its potential across various quantum computing applications. © 2024 IEEE.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8