Optimització de l'entrenabilitat i l'expressivitat per a VQAs informats per la física
Year: 2026
Type: dissertation
Abstract: [EN] This bachelor’s thesis explores parameterized quantum circuits (PQCs) or ansatzes in variational quantum algorithms (VQAs) for preparing the ground state of different Hamiltonians, comparing them using trainability and expressiveness metrics. Based on these results, the study examines how the structure of the ansatz—if selected correctly—can improve both properties simultaneously by incorporating symmetries of the Hamiltonian to constrain the search space and facilitate training. Finally, the impact of these physics-informed versions is evaluated against standard, state-of-the-art designs.
Author: Martín-Kruglova, Víctor
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225666790