Component Based Quantum Machine Learning Explainability

2025-06-14

AI Summary

Quantum machine learning (QML) models suffer from poor interpretability—due to their black-box nature—posing significant challenges for regulatory compliance (e.g., GDPR) and trust in high-stakes domains such as healthcare and finance. To address this, we propose a modular, interpretable QML framework that decouples QML into distinct components: quantum feature mapping, variational quantum circuits (ansätze), and classical optimizers, enabling granular attribution analysis per module. Crucially, we extend classical interpretability methods—specifically Accumulated Local Effects (ALE) and SHAP—to quantum-native components, including parameterized quantum feature maps and ansätze, thereby enabling end-to-end local explanations. The framework supports bias detection and decision traceability. We validate it on simulated financial credit scoring and disease prediction tasks, demonstrating both empirical effectiveness and robust compliance support. Our approach substantially enhances transparency, auditability, and trustworthiness of QML systems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Security and Privacy: Security and privacy of machine learning and AI applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Developing explainable techniques for quantum machine learning models
• Analyzing QML components like feature maps and variational circuits
• Ensuring transparency in QML for healthcare and finance applications

Innovation

• Modular QML framework with core components
• Explainability techniques like ALE and SHAP
• Analyzing quantum-classical loops for transparency