Machine Learning Applications in Quantum Computing

This technical report discusses the intersection of machine learning and quantum computing, particularly focusing on quantum machine learning (QML). It outlines the progress made in QML over the past five years, moving from theoretical studies to practical applications demonstrated on contemporary quantum devices. Use cases include classifying medical images and generating handwritten images, among others. The report highlights potential benefits of QML, such as faster training and the discovery of feature maps that are not identifiable through classical methods. It also addresses the current limitations of QML, emphasizing the challenges posed by Noisy Intermediate Scale Quantum devices and the need for fault-tolerant quantum computers for practical applications. Additionally, the report provides a foundational overview of machine learning concepts necessary for understanding QML, including linear algebra and probability, while recommending further reading for deeper insights. The document serves as an introductory guide for those with a basic understanding of quantum computing who wish to explore machine learning in this context.