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.

这篇论文简要介绍了量子机器学习的潜在好处，探讨了使用量子计算原理和算法可能改进传统机器学习方法的潜力。论文涵盖了量子力学的基本原理，包括叠加态、相位空间和纠缠，介绍了利用这些特性的量子门的概念。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，然后深入探讨了可训练的量子电路作为神经网络的概念。通过一个例子问题展示了量子神经网络的潜在优势，并提供了详细的推导附录。该论文旨在帮助新近接触量子力学和机器学习的研究人员更高效地发展自己的专业知识。