QuXAI: Explainers for Hybrid Quantum Machine Learning Models
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.
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Saikat Barua
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Saikat Barua
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AlignAI
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Mostafizur Rahman
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Mostafizur Rahman
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imostafizur
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Shehenaz Khaled
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Md Jafor Sadek
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MD Jafor Sadek Khan
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Jafor
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Rafiul Islam
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Shahnewaz Siddique
QuXAI: Explainers for Hybrid Quantum Machine Learning Models
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.
QuXAI framework leverages Q-MEDLEY to improve explainability and interpretability of hybrid quantum-classical machine learning models by delineating influential classical aspects and separating noise.
hybrid quantum-classical machine learning HQML quantum feature maps Q-MEDLEY feature importance XAI quantum systems explainability interpretability ablation studies