Primer on Data in Quantum Machine Learning

Aviral Shrivastava, Vinayak Sharma

Abstract: With the increased interest in Quantum Machine Learning (QML), the integration of classical data into quantum systems presents unique challenges and opportunities. The class "Primer on Data in Quantum Machine Learning" delves into the foundational concepts and advanced techniques of embedding classical data into quantum states, a critical process for enhancing the performance of quantum algorithms. By exploring various quantum embedding methods and understanding their strengths and limitations, participants will gain a comprehensive understanding of the impact quantum embeddings can have on machine learning applications. This lesson will cover the following concepts: Fundamental Concepts of Quantum Machine Learning, Limits of NISQ devices and Computing in the NISQ era, Embeddings for QML, and Practical effects of embeddings. The understanding of these topics should provide a better understanding of the importance and effect of embeddings on the overall performance of QML in the NISQ era.