Quantum Computing and Machine Learning in the NISQ Era

Abstract

This research project aims to explore the intersection of quantum computing and machine learning in the Noisy Intermediate-Scale Quantum (NISQ) era, which has opened up new opportunities for machine learning. We will investigate the current state of quantum computing, its advantages over classical computing, and the current limitations of NISQ devices. Specifically, we will examine different approaches and models to combine quantum computing and machine learning, including quantum machine learning algorithms, hybrid classical-quantum algorithms, and quantum-inspired classical algorithms in various domains, seeking for potential applications. Our approach involves trying a large variety of models and datasets and, as part of this research, we will provide a repository of the trained models to promote transparency and reproducibility in the field and to facilitate further research in this area. The interface between quantum computing and machine learning is an exciting area of research with the potential to unlock new capabilities for solving complex problems in diverse areas and has also implications for national security and sovereignty, therefore their growing importance in Brazil merits full attention. We believe that our work will contribute significantly to advancing this field. (AU)