Quantum Machine Learning in Anomaly Detection: Benchmarking, Noise Mitigation and Applications.

• Grant number: 24/14934-0
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: July 01, 2025
• End date: June 30, 2028
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Kelton Augusto Pontara da Costa
• Grantee: Felipe Rodrigues Perche Mahlow
• Host Institution: Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil
• Associated research grant: 23/12830-0 - Quantum Intelligent Systems for Cybersecurity, AP.R

Abstract

Quantum computing and machine learning have emerged as promising technologies capable of transforming various sectors of society. The fusion of these disciplines has given rise to the field of Quantum Machine Learning (QML), which aims to explore the synergies between quantum theory and machine learning methods to solve complex problems more efficiently. Specifically, anomaly detection, essential in areas such as cybersecurity, industrial monitoring, finance, and healthcare, faces challenges related to scalability and data complexity. Traditional machine learning methods, although widely used, have limitations when dealing with large volumes of data and high dimensionality. This postdoctoral research project aims to investigate and contribute to the development of QML algorithms focused on anomaly detection in complex systems. The research will focus on evaluating the performance of quantum algorithms in both quantum simulators and real hardware, conducting extensive benchmarking against traditional machine learning methods. Furthermore, the project will explore noise mitigation techniques to enhance the robustness of quantum algorithms and apply these tech- niques to real world case studies, such as intrusion detection in computer networks. The results will be disseminated through scientific publications and presentations at international conferences, contributing to the advancement of knowledge in QML and its practical appli- cation in anomaly detection.