Machine Learning (ML) models are trained using historical data to classify new, unseen data. However, traditional computing resources often struggle to handle the immense amount of data, commonly known as Big Data, within a reasonable time frame. Quantum Computing (QC) provides a novel approach to information processing, offering the potential to process classical data exponentially faster than classical computing through quantum algorithms. By mapping Quantum Machine Learning (QML) algorithms into the quantum mechanical domain, we can potentially achieve exponential improvements in data processing speed, reduced resource requirements, and enhanced accuracy and efficiency. In this article, we delve into both the QC and ML fields, exploring the interplay of ideas between them, as well as the current capabilities and limitations of hardware. We investigate the history of quantum computing, examine existing QML algorithms, and present a simplified procedure for setting up simulations of QML algorithms, making it accessible and understandable for readers. Furthermore, we conduct simulations on a dataset using both traditional machine learning and quantum machine learning approaches. We then compare their respective performances by utilizing a quantum simulator.

翻译：机器学习（ML）模型通过历史数据进行训练，以对新出现的未知数据进行分类。然而，传统计算资源往往难以在合理的时间范围内处理海量数据（通常称为大数据）。量子计算（QC）提供了一种新颖的信息处理方法，通过量子算法，有望以指数级速度超越经典计算来处理经典数据。通过将量子机器学习（QML）算法映射到量子力学领域，我们有望在数据处理速度上实现指数级提升，同时降低资源需求，并提高准确性与效率。本文深入探讨了量子计算与机器学习领域，探索两者之间的思想交融，以及当前硬件的性能与局限。我们回顾了量子计算的发展历程，审视了现有的量子机器学习算法，并提出了一种简化的量子机器学习算法模拟设置流程，旨在使读者易于理解与掌握。此外，我们分别采用传统机器学习与量子机器学习方法对数据集进行了模拟实验，并利用量子模拟器对两者的性能进行了比较分析。