Quantum Machine Learning: Variational Algorithms and Their Applications

• Grant number: 25/08578-9
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• Start date: July 31, 2025
• End date: January 30, 2026
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Felipe Fernandes Fanchini
• Grantee: Pedro Marcelo Prado
• Supervisor: Ariel Ignacio Norambuena Zamorano
• Host Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
• Institution abroad: Universidad Técnica Federico Santa María (USM), Chile
• Associated to the scholarship: 23/12110-7 - Optimization and Quantum Machine Learning: Variational Algorithms and Their Applications., BP.MS

Abstract

This project aims to study the application of quantum machine learning (QML) algorithms, focusing on QSVM (Quantum Support Vector Machine) and QPINN (Quantum Physics-Informed Neural Network) models. The objective is to understand their capabilities, limitations, and explanatory potential in small-scale quantum systems. An exploratory analysis of circuit structures and their behavior in classification and regression tasks will be conducted, supported by Explainable AI (XAI) tools to enhance model interpretability and performance. Noise will be considered as a secondary factor to assess its impact on algorithm robustness. The research will be carried out at the Universidad Técnica Federico Santa María, in Chile, in collaboration with a research group specializing in open quantum systems. The expected outcomes include identifying structural patterns, proposing adaptations to reduce qubit requirements, and strengthening the theoretical and experimental basis for practical QML applications. (AU)