Introduction to Quantum Machine Learning and Quantum Architecture Search

Introduction to Quantum Machine Learning and Quantum Architecture Search Samuel Y en-Chi Chen 1 Zhiding Liang 2 1 Wells Fargo 2 Rensselaer Polytechnic Institute Abstract --Recent advancements in quantum computing (QC) and machine learning (ML) have fueled significant research efforts aimed at integrating these two transformative technologies. Quantum machine learning (QML), an emerging interdisciplinary field, leverages quantum principles to enhance the performance of ML algorithms. Concurrently, the exploration of systematic and automated approaches for designing high-performance quantum circuit architectures for QML tasks has gained prominence, as these methods empower researchers outside the quantum computing domain to effectively utilize quantum-enhanced tools. This tutorial will provide an in-depth overview of recent breakthroughs in both areas, highlighting their potential to expand the application landscape of QML across diverse fields. I NTRODUCTION Quantum computing (QC) offers the potential for substantial speedups in solving certain computationally challenging problems compared to classical computers. Recent advancements in quantum hardware, coupled with remarkable progress in classical AI and machine learning (ML) techniques, have sparked growing interest in merging these two technologies to further accelerate advancements in artificial intelligence.