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.
Abstract (translated)
最近在量子计算（QC）和机器学习（ML）领域的进展激发了大量旨在将这两种变革性技术融合的研究努力。量子机器学习（QML），作为一门新兴的跨学科领域，利用量子原理来提升机器学习算法的表现。同时，为设计高性能量子电路架构以应对QML任务而探索系统化与自动化的方法也日益受到重视，这些方法使非量子计算领域的研究人员也能有效地使用增强型量子工具。本教程将全面介绍这两个领域的最新突破，并强调它们在扩展QML应用领域方面的潜力，尤其是在不同学科中的应用。
URL
https://arxiv.org/abs/2504.16131
