Optimization and Quantum Machine Learning: Variational Algorithms and Their Applications.

• Grant number: 23/12110-7
• Support Opportunities: Scholarships in Brazil - Master
• Start date: February 01, 2024
• End date: January 31, 2026
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Felipe Fernandes Fanchini
• Grantee: Pedro Marcelo Prado
• Host Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil

Abstract

The objective of this study is to analyze how quantum algorithms can be used to enhance optimization and machine learning. We will explore how these algorithms operate in open quantum systems and how they can be combined with traditional machine learning methods to improve their outcomes. Furthermore, we will investigate how quantum algorithms can be applied in simpler scenarios of open quantum systems to gain a better understanding of their functionality. We will commence by examining specific algorithms such as QAOA and FALQON and comprehending their characteristics and limitations. Next, we will delve into how we can integrate quantum algorithms with classical optimization methods to enhance the efficiency and accuracy of quantum methods. To provide more clarity to our findings, we will analyze real-world examples of applying these techniques, especially in the fields of logistics and finance. This will enable us to see how variational algorithms and hybrid approaches can be valuable in practical situations. When working with real cases, we will be able to assess better how these strategies perform in open quantum systems and understand the challenges that may arise. Ultimately, we hope that these case studies will help us gain a better understanding of how to utilize the proposed techniques, especially concerning open quantum systems. We will also be able to identify future research opportunities based on the challenges we encounter and the possibilities that emerge.