An Application of Pontryagin Neural Networks to Solve Optimal Quantum Control Problems

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

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

Ata de Conferência Internacional

Título: 9th International Conference on Control, Decision and Information Technologies, CoDIT 2023, Rome, Italy, July 3-6, 2023

Páginas: 202-207

9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

Rome, 3 July 2023 through 6 July 2023

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Z-DCG

DOI: 10.1109/codit58514.2023.10284049

Abstract (EN): Reliable high-fidelity quantum state transformation has always been considered as an inseparable part of quantum information processing. In this regard, Pontryagin Minimum (or maximum) Principle (PMP) has proved to play an important role to achieve the maximum fidelity in an optimum time or energy. Motivated by this, in this work, we formulate a control constrained optimal control problem where we aim to minimize time and also energy subjected to a quantum system satisfying the bilinear Schrödinger equation. We derive the first order optimality conditions through the application of PMP resulting in a boundary value problem. Next, in order to obtain efficient numerical results, we exploit a particular family of physics-informed neural networks that are specifically designed to tackle the indirect method based on the PMP. We show that this method can significantly speed up the process by first obtaining a set of relations which finally let us compute the optimal control strategy to determine the time- and energy-optimal protocol driving a general initial state to a target state by a quantum Hamiltonian with bounded control. We make use of the so-called 'qutip' package in python, and the newly developed 'tfc' python package. © 2023 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5