This paper provides an introduction to quantum machine learning, exploring the potential benefits of using quantum computing principles and algorithms that may improve upon classical machine learning approaches. Quantum computing utilizes particles governed by quantum mechanics for computational purposes, leveraging properties like superposition and entanglement for information representation and manipulation. Quantum machine learning applies these principles to enhance classical machine learning models, potentially reducing network size and training time on quantum hardware. The paper covers basic quantum mechanics principles, including superposition, phase space, and entanglement, and introduces the concept of quantum gates that exploit these properties. It also reviews classical deep learning concepts, such as artificial neural networks, gradient descent, and backpropagation, before delving into trainable quantum circuits as neural networks. An example problem demonstrates the potential advantages of quantum neural networks, and the appendices provide detailed derivations. The paper aims to help researchers new to quantum mechanics and machine learning develop their expertise more efficiently.

翻译：本文介绍量子机器学习，探讨利用量子计算原理及算法以改进传统机器学习方法的潜在优势。量子计算利用受量子力学支配的粒子进行信息处理，通过叠加态和纠缠等特性实现信息表征与操作。量子机器学习应用这些原理增强传统机器学习模型，有望在量子硬件上减少网络规模并缩短训练时间。本文涵盖量子力学基础原理（包括叠加态、相空间与纠缠），并介绍利用这些特性的量子门概念。在探讨可训练量子电路作为神经网络之前，本文还回顾了经典深度学习概念（如人工神经网络、梯度下降和反向传播）。通过示例问题展示量子神经网络的潜在优势，附录部分提供详细推导。本文旨在帮助量子力学与机器学习领域的新研究者更高效地构建专业知识体系。