Title: Component Based Quantum Machine Learning Explainability Show Chinese title

Title: 基于组件的量子机器学习可解释性

Abstract: Explainable ML algorithms are designed to provide transparency and insight into their decision-making process. Explaining how ML models come to their prediction is critical in fields such as healthcare and finance, as it provides insight into how models can help detect bias in predictions and help comply with GDPR compliance in these fields. QML leverages quantum phenomena such as entanglement and superposition, offering the potential for computational speedup and greater insights compared to classical ML. However, QML models also inherit the black-box nature of their classical counterparts, requiring the development of explainability techniques to be applied to these QML models to help understand why and how a particular output was generated. This paper will explore the idea of creating a modular, explainable QML framework that splits QML algorithms into their core components, such as feature maps, variational circuits (ansatz), optimizers, kernels, and quantum-classical loops. Each component will be analyzed using explainability techniques, such as ALE and SHAP, which have been adapted to analyse the different components of these QML algorithms. By combining insights from these parts, the paper aims to infer explainability to the overall QML model. Show Chinese abstract

Abstract: 可解释的机器学习算法旨在为其决策过程提供透明性和洞察力。 解释机器学习模型如何做出预测，在医疗保健和金融等领域至关重要，因为它可以提供关于模型如何帮助检测预测中的偏见，并在这些领域帮助遵守GDPR合规性的见解。 量子机器学习（QML）利用纠缠和叠加等量子现象，与经典机器学习相比，提供了计算加速和更深入洞察的可能性。 然而，QML模型也继承了其经典对应物的黑箱特性，需要开发可解释性技术来应用于这些QML模型，以帮助理解特定输出是如何生成的。 本文将探讨创建一个模块化的、可解释的QML框架的想法，该框架将QML算法分解为其核心组件，例如特征映射、变分电路（ansatz）、优化器、核函数以及量子-经典循环。 每个组件将使用可解释性技术进行分析，例如ALE和SHAP，这些技术已被改编以分析这些QML算法的不同组件。 通过结合这些部分的见解，本文旨在将可解释性推导到整个QML模型中。