QuXAI：混合量子機器學習模型的解釋器

QuXAI: Explainers for Hybrid Quantum Machine Learning Models

May 15, 2025

作者: Saikat Barua, Mostafizur Rahman, Shehenaz Khaled, Md Jafor Sadek, Rafiul Islam, Shahnewaz Siddique

cs.AI

摘要

混合量子-經典機器學習（HQML）模型的出現開闢了計算智能的新視野，但其固有的複雜性往往導致黑箱行為，從而削弱了其應用中的透明度和可靠性。儘管針對量子系統的可解釋人工智能（XAI）仍處於起步階段，但在為採用量子特徵編碼後接經典學習的HQML架構設計的穩健全局和局部可解釋性方法方面，存在明顯的研究空白。這一空白正是本工作的焦點，我們引入了基於Q-MEDLEY的QuXAI框架，這是一種用於解釋這些混合系統中特徵重要性的解釋器。我們的模型包括創建結合量子特徵映射的HQML模型，使用Q-MEDLEY，它結合了基於特徵的推理，保留了量子轉換階段並可視化最終的歸因結果。我們的結果表明，Q-MEDLEY不僅能勾勒出HQML模型中具有影響力的經典方面，還能分離其噪聲，並在經典驗證設置中與現有的XAI技術競爭良好。消融研究更顯著地揭示了Q-MEDLEY中使用的複合結構的優點。這項工作的意義至關重要，因為它提供了一條提高HQML模型可解釋性和可靠性的途徑，從而促進更大的信心，並能夠更安全、更負責任地使用量子增強的人工智能技術。

English

The emergence of hybrid quantum-classical machine learning (HQML) models opens new horizons of computational intelligence but their fundamental complexity frequently leads to black box behavior that undermines transparency and reliability in their application. Although XAI for quantum systems still in its infancy, a major research gap is evident in robust global and local explainability approaches that are designed for HQML architectures that employ quantized feature encoding followed by classical learning. The gap is the focus of this work, which introduces QuXAI, an framework based upon Q-MEDLEY, an explainer for explaining feature importance in these hybrid systems. Our model entails the creation of HQML models incorporating quantum feature maps, the use of Q-MEDLEY, which combines feature based inferences, preserving the quantum transformation stage and visualizing the resulting attributions. Our result shows that Q-MEDLEY delineates influential classical aspects in HQML models, as well as separates their noise, and competes well against established XAI techniques in classical validation settings. Ablation studies more significantly expose the virtues of the composite structure used in Q-MEDLEY. The implications of this work are critically important, as it provides a route to improve the interpretability and reliability of HQML models, thus promoting greater confidence and being able to engage in safer and more responsible use of quantum-enhanced AI technology.