Quantum optimization and machine learning: variational algorithms and applications

• Grant number: 23/04987-6
• Support Opportunities: Regular Research Grants
• Start date: October 01, 2023
• End date: September 30, 2025
• 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 investigate the potential and applications of quantum algorithms in optimization and machine learning, with a focus on open quantum systems and the integration with classical machine learning techniques to improve the performance of these algorithms. In addition, the project also aims to study simple quantum machine learning problems considering open quantum systems. The research will explore specific case studies to demonstrate the advantages and challenges associated with this hybrid approach. First, the project will analyze several algorithms, in particular QAOA and FALQON, examining the properties and limitations of these algorithms. Next, the research will investigate the combination of classical machine learning techniques, such as Support Vector Machines and neural networks, with quantum algorithms, in order to improve the efficiency and accuracy of quantum methods. Finally, the project will explore a series of case studies that illustrate the practical application of variational algorithms and hybrid techniques, particularly in the area of logistics and finance. These case studies will allow us to evaluate the effectiveness of the proposed approaches, especially in open quantum systems, and to identify possible challenges and opportunities for future research. (AU)

Scientific publications

(6)

(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)

SUDA NETO, JOGI; ARDILA, LLUIS QUILES; NOGUEIRA, THIAGO NASCIMENTO; ALBUQUERQUE, FELIPE; PAPA, JOAO PAULO; GUIDO, RODRIGO CAPOBIANCO; FANCHINI, FELIPE FERNANDES. Quantum neural networks successfully calibrate language models. QUANTUM MACHINE INTELLIGENCE, v. 6, n. 1, p. 9-pg., 2024-06-01. (21/12407-4, 19/18287-0, 14/12236-1, 21/04655-8, 19/07665-4, 23/04987-6, 13/07375-0)

PEXE, G. E. L.; RATTIGHIERI, L. A. M.; MALVEZZI, A. L.; FANCHINI, F. F.. Using a feedback-based quantum algorithm to analyze the critical properties of the ANNNI model without classical optimization. PHYSICAL REVIEW B, v. 110, n. 22, p. 13-pg., 2024-12-16. (23/04987-6)

DA SILVA, ADONAI HILARIO; NAPOLITANO, REGINALDO DE JESUS; FANCHINI, FELIPE FERNANDES; BELLOMO, BRUNO. Time-dependent Rabi frequencies to protect quantum operations on an atomic qutrit by continuous dynamical decoupling. PHYSICAL REVIEW A, v. 109, n. 3, p. 13-pg., 2024-03-13. (18/00796-3, 23/04987-6)

BARRETO, ARTUR GOMES; FANCHINI, FELIPE FERNANDES; PAPA, JOAO PAULO; ALBUQUERQUE, VICTOR HUGO C. DE. Why consider quantum instead classical pattern recognition techniques?. APPLIED SOFT COMPUTING, v. 165, p. 11-pg., 2024-08-21. (23/14427-8, 23/04987-6, 13/07375-0, 23/12830-0)

FERNANDES, MARLLOS E. F.; FANCHINI, FELIPE F.; DE LIMA, EMANUEL F.; CASTELANO, LEONARDO K.. Effectiveness of the Krotov method in finding controls for open quantum systems. Journal of Physics A-Mathematical and Theoretical, v. 56, n. 49, p. 14-pg., 2023-12-08. (23/04987-6, 19/09624-3, 14/23648-9, 21/04655-8)

DA COSTA MORAZOTTI, NICOLAS ANDRE; DA SILVA, ADONAI HILARIO; AUDI, GABRIEL; FANCHINI, FELIPE FERNANDES; DE JESUS NAPOLITANO, REGINALDO. Optimized continuous dynamical decoupling via differential geometry and machine learning. PHYSICAL REVIEW A, v. 110, n. 4, p. 14-pg., 2024-10-01. (18/00796-3, 23/04987-6)