Debus Pascal, Issel Sebastian, Tscharke Kilian
IEEE Comput Graph Appl. 2024 Sep-Oct;44(5):40-53. doi: 10.1109/MCG.2024.3456288. Epub 2024 Oct 25.
This article introduces an innovative interactive visualization tool designed to demystify quantum machine learning (QML) algorithms. Our work is inspired by the success of classical machine learning visualization tools, such as TensorFlow Playground, and aims to bridge the gap in visualization resources specifically for the field of QML. The article includes a comprehensive overview of relevant visualization metaphors from both quantum computing and classical machine learning, the development of an algorithm visualization concept, and the design of a concrete implementation as an interactive web application. By combining common visualization metaphors for the so-called data reuploading universal quantum classifier as a representative QML model, this article aims to lower the entry barrier to quantum computing and encourage further innovation in the field. The accompanying interactive application is a proposal for the first version of a QML playground for learning and exploring QML models.
本文介绍了一种创新的交互式可视化工具，旨在揭开量子机器学习（QML）算法的神秘面纱。我们的工作受到了经典机器学习可视化工具（如TensorFlow游乐场）成功的启发，旨在弥合专门针对QML领域的可视化资源差距。本文全面概述了来自量子计算和经典机器学习的相关可视化隐喻，算法可视化概念的开发，以及作为交互式Web应用程序的具体实现设计。通过将通用可视化隐喻应用于作为代表性QML模型的所谓数据重新上传通用量子分类器，本文旨在降低量子计算的入门门槛，并鼓励该领域的进一步创新。随附的交互式应用程序是关于用于学习和探索QML模型的QML游乐场第一版的提案。
