论文标题
单个量子的量子机学习的教学方法
A didactic approach to quantum machine learning with a single qubit
论文作者
论文摘要
本文通过一个带有现实世界数据集的明确示例，对量子机学习领域（QML）的动手简介。我们使用数据重新上传技术专注于单个量子的学习情况。在讨论了量子计算和机器学习的相关背景之后，我们提供了我们考虑的数据重新上传模型的详尽说明，并使用Qiskit量子计算SDK在玩具和现实数据集中实施了不同的提出的公式。我们发现，与经典神经网络一样，层数是模型最终准确性的决定因素。此外，有趣的是，结果表明，单品分类器可以在相同的一组训练条件下实现与经典同行相对应的性能。尽管这不能被理解为量子机学习优势的证明，但它指向了有希望的研究方向，并提出了一系列我们概述的问题。
This paper presents, via an explicit example with a real-world dataset, a hands-on introduction to the field of quantum machine learning (QML). We focus on the case of learning with a single qubit, using data re-uploading techniques. After a discussion of the relevant background in quantum computing and machine learning we provide a thorough explanation of the data re-uploading models that we consider, and implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK. We find that, as in the case of classical neural networks, the number of layers is a determining factor in the final accuracy of the models. Moreover, and interestingly, the results show that single-qubit classifiers can achieve a performance that is on-par with classical counterparts under the same set of training conditions. While this cannot be understood as a proof of the advantage of quantum machine learning, it points to a promising research direction, and raises a series of questions that we outline.
