Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/215535
Title: Quantum Reservoir Computing for Hamiltonian Learning in Metal-Insulator Anderson Transitions
Author: Cortés Páez, Lucía
Director/Tutor: Mujal Torreblanca, Pere
Juliá-Díaz, Bruno
Keywords: Ordinadors quàntics
Aprenentatge automàtic
Treballs de fi de grau
Quantum computers
Machine learning
Bachelor's theses
Issue Date: Jun-2024
Abstract: This research investigates transport regimes in metal-insulator Anderson transition through Hamiltonian learning. Quantum reservoir computing is employed to estimate the stochasticity parameter in the Hamiltonian of the quasiperiodic kicked rotor, a model that displays Anderson transition in momentum space. The stochasticity parameter is key for classifying phase regimes, i.e., localized/insulator phase, delocalized/metalic phase, and critical phase, as well as qualitatively forecasting trajectory evolution. Thus, supervised machine learning that effectively maps input trajectories to their corresponding stochasticity parameter has been developed, highlighting the efficacy of quantum machine learning in analyzing quantum phenomena
Note: Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2024, Tutors: Pere Mujal Torreblanca, Bruno Juliá Díaz
URI: https://hdl.handle.net/2445/215535
Appears in Collections:Treballs Finals de Grau (TFG) - Física

Files in This Item:
File Description SizeFormat 
CORTÉS PÁEZ LUCÍA.pdf2.46 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons