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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Dehaghani, NB (Autor) Outra; Aguiar, AP (Autor) FEUP; Wisniewski, R (Autor) Outra

Ata de Conferência Internacional

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

Páginas: 1378-1386

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

Montreal, 15 September 2024 through 20 September 2024

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8