Quantum machine learning (QML) is a promising early use case for quantum computing. There has been progress in the last five years from theoretical studies and numerical simulations to proof of concepts. Use cases demonstrated on contemporary quantum devices include classifying medical images and items from the Iris dataset, classifying and generating handwritten images, toxicity screening, and learning a probability distribution. Potential benefits of QML include faster training and identification of feature maps not found classically. Although, these examples lack the scale for commercial exploitation, and it may be several years before QML algorithms replace the classical solutions, QML is an exciting area. This article is written for those who already have a sound knowledge of quantum computing and now wish to gain a basic overview of the terminology and some applications of classical machine learning ready to study quantum machine learning. The reader will already understand the relevant relevant linear algebra, including Hilbert spaces, a vector space with an inner product.

翻译：量子机器学习（QML）是量子计算中一个颇具前景的早期应用场景。过去五年间，该领域已从理论研究和数值模拟发展到概念验证阶段。在当代量子设备上展示的应用案例包括：对医学图像和鸢尾花数据集进行分类、手写图像的分类与生成、毒性筛选以及概率分布学习。QML的潜在优势包括更快的训练速度以及识别经典方法无法发现的特征映射。尽管这些示例尚未达到商业开发利用的规模，且可能需要数年时间QML算法才能替代经典解决方案，但QML仍是一个令人兴奋的研究领域。本文面向已具备扎实量子计算知识、希望初步了解经典机器学习术语及应用的读者，为其后续学习量子机器学习奠定基础。读者应已掌握相关线性代数知识，包括内积向量空间——希尔伯特空间。