介绍量子机器学习与自动化量子电路设计方法
Introduction to Quantum Machine Learning and Quantum Architecture Search
- 结合量子计算与机器学习，提升算法性能
- 提出自动化设计高效率量子电路架构的方法
- 适合非量子领域研究者快速入门应用
量子计算（QC）与机器学习（ML）的最新进展推动了两者融合的深入研究。量子机器学习（QML）作为新兴交叉领域，利用量子原理增强机器学习算法表现。同时，针对QML任务系统化、自动化的高性能量子电路架构设计方法日益受到重视，使非量子计算领域的研究人员也能有效使用量子增强工具。本教程将深入综述两方面的最新突破，突出其在拓展QML应用范围方面的潜力，覆盖多个不同领域。
原文摘要 · Abstract (English)
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.
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