Quantum Algorithms for solving Complex Problems and Commercial Applications

• Grant number: 23/15739-3
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: December 01, 2023
• End date: November 30, 2025
• Field of knowledge: Physical Sciences and Mathematics - Physics
• Principal Investigator: Celso Jorge Villas-Bôas
• Grantee: Tiago de Souza Farias
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Associated research grant: 22/00209-6 - Second generation quantum technologies, AP.TEM

Abstract

In this project we will work with quantum algorithms for solving optimization (travelling salesman problem) and fluid dynamics problems (systems of coupled differential equations and partial differential equations), aiming to determine their overall complexities, in order to be able to precisely point out what are the advantages and disadvantages of such algorithms and what are the necessary hardware requirements for real gains from such algorithms in the future, when compared to the best existing classical algorithms. The project is also focused on practical problems, of commercial interest, e.g., vehicle routing, facility allocation, fluid flow (as in oil extraction problems, for example), etc. Finally, we are also interested in searching for more efficient algorithms, either quantum, quantum inspired or hybrid (part solved in quantum computers and part in classical ones), aiming to present the best solution for those practical problems.

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)

FRIEDRICH, LUCAS; FARIAS, TIAGO DE SOUZA; MAZIERO, JONAS. Barren plateaus are amplified by the dimension of qudits. QUANTUM MACHINE INTELLIGENCE, v. 7, n. 1, p. 17-pg., 2025-06-01. (23/15739-3)

FARIAS, TIAGO S.; SCHULTZ, VITOR V.; MOMBACH, JOSE C. M.; MAZIERO, JONAS. A differentiable programming framework for spin models. COMPUTER PHYSICS COMMUNICATIONS, v. 302, p. 12-pg., 2024-05-20. (23/15739-3)