Machine learning and its applications in quantum physics: metrology, thermology, phase transitions and dynamical decoupling

• Grant number: 21/04655-8
• Support Opportunities: Regular Research Grants
• Duration: October 01, 2021 - September 30, 2023
• Field of knowledge: Physical Sciences and Mathematics - Physics - Atomic and Molecular Physics
• Principal Investigator: Felipe Fernandes Fanchini
• Grantee: Felipe Fernandes Fanchini
• Host Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
• Associated researchers: João Paulo Papa

Abstract

This research project aims to develop a methodology that can be applied to four main themes of quantum physics: quantum metrology, quantum thermology, error protection techniques and quantum phase transitions. We will focus on several machine learning techniques, considering from the simplest to the most sophisticated models. Concerning quantum metrology, we will focus on the study of quantum dots, trying to develop an algorithm capable of determining the constant coupling between two qubits. In thermology, our objective will be to determine the temperature of a reservoir by means of an auxiliar system. In the case of phase transitions, our focus will be on the development of a universal classifier, that is, an algorithm capable of detecting the phases even of unknown models. Finally, we will make use of machine learning to optimize quantum information protection techniques, in particular dynamical decoupling. During the development of this project, we intend to introduce new routines for the study of metrology, thermology, quantum phase transitions and protection techniques guided now by computational methods based on machine learning. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

TANCARA, DIEGO; DINANI, HOSSEIN T.; NORAMBUENA, ARIEL; FANCHINI, FELIPE F.; COTO, RAUL. Kernel-based quantum regressor models learning non-Markovianity. HYSICAL REVIEW, v. 107, n. 2, p. 10-pg., 2023-02-03. (21/04655-8)

LUIZ, FABRICIO S.; DE OLIVEIRA, JR., A.; FANCHINI, FELIPE F.; LANDI, GABRIEL T.. achine classification for probe-based quantum thermometr. HYSICAL REVIEW, v. 105, n. 2, FEB 10 2022. (21/04655-8, 17/50304-7, 17/07973-5, 18/12813-0)

CADORIM, LEONARDO RODRIGUES; TOLEDO, LUCAS VENEZIANI DE; ORTIZ, WILSON AIRES; BERGER, JORGE; SARDELLA, EDSON. Closed vortex state in three-dimensional mesoscopic superconducting films under an applied transport current. PHYSICAL REVIEW B, v. 107, n. 9, p. 8-pg., 2023-03-20. (20/03947-2, 21/04655-8, 19/24618-0, 20/10058-0)