拆解量子机器学习模型组件，实现可解释性分析。
Component Based Quantum Machine Learning Explainability
- 将量子机器学习拆分为特征映射、变分电路等模块分别分析。
- 采用适配的SHAP和ALE方法解析各组件对输出的影响。
- 适合关注量子模型可解释性的研究人员与开发者。
可解释机器学习算法旨在揭示其决策过程的透明性。在医疗和金融等领域，理解模型如何做出预测至关重要，有助于发现预测中的偏见并满足GDPR合规要求。量子机器学习（QML）利用量子纠缠和叠加等现象，相比经典机器学习具有潜在计算加速和更深入洞察的优势。然而，QML模型也继承了经典模型的黑箱特性，亟需可解释性技术来理解特定输出的生成原因。本文提出构建一个模块化的可解释量子机器学习框架，将QML算法分解为特征映射、变分电路（ansatz）、优化器、核函数及量子-经典循环等核心组件。通过适配的可解释性方法（如ALE和SHAP）对各组件进行分析，结合各部分的洞察，最终实现对整体QML模型的可解释性推断。
原文摘要 · Abstract (English)
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.
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