Physics-informed neural networks for an optimal counterdiabatic quantum computation

A novel methodology that leverages physics-informed neural networks to optimize quantum circuits in systems with $\mathrm{N}_{\mathrm{Q}}$ qubits by addressing the counterdiabatic (CD) protocol is introduced. The primary purpose is to employ physics-inspired deep learning techniques for accurately m...

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Autors principals:	Antonio Ferrer-Sánchez, Carlos Flores-Garrigos, Carlos Hernani-Morales, José J Orquín-Marqués, Narendra N Hegade, Alejandro Gomez Cadavid, Iraitz Montalban, Enrique Solano, Yolanda Vives-Gilabert, José D Martín-Guerrero
Format:	Artigo
Idioma:	Inglês
Publicat:	IOP Publishing 2024-01-01
Col·lecció:	Machine Learning: Science and Technology
Matèries:	physics-informed neural networks quantum computing shortcuts to adiabaticity counterdiabatic driving Pauli operators
Accés en línia:	https://doi.org/10.1088/2632-2153/ad450f
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