title: QuXAI: Explainers for Hybrid Quantum Machine Learning Models
summary: 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.
paper_authors._id: 68271b6ed5568917361eb8d8
paper_authors.name: Saikat Barua
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-16T11:03:11.305Z
paper_authors._id: 653425f4ed74ace63395826c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/QJlB0DOEel6U9b-95wasK.png
paper_authors.fullname: Saikat Barua
paper_authors.type: user
paper_authors.user: AlignAI
paper_authors._id: 68271b6ed5568917361eb8d9
paper_authors.name: Mostafizur Rahman
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-05-16T11:03:11.305Z
paper_authors._id: 63827fb4544214606661792d
paper_authors.avatarUrl: /avatars/bc47b0733cc034ec006ebaca803fef62.svg
paper_authors.fullname: Mostafizur Rahman
paper_authors.type: user
paper_authors.user: imostafizur
paper_authors._id: 68271b6ed5568917361eb8da
paper_authors.name: Shehenaz Khaled
paper_authors._id: 68271b6ed5568917361eb8db
paper_authors.name: Md Jafor Sadek
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-16T14:18:14.383Z
paper_authors._id: 63c99ab3dfac8071d01b61d4
paper_authors.avatarUrl: /avatars/9151241b8af4d64d7771740587d1b7a5.svg
paper_authors.fullname: MD Jafor Sadek Khan
paper_authors.type: user
paper_authors.user: Jafor
paper_authors._id: 68271b6ed5568917361eb8dc
paper_authors.name: Rafiul Islam
paper_authors._id: 68271b6ed5568917361eb8dd
paper_authors.name: Shahnewaz Siddique
paper_title: QuXAI: Explainers for Hybrid Quantum Machine Learning Models
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: hybrid quantum-classical machine learning HQML quantum feature maps Q-MEDLEY feature importance XAI quantum systems explainability interpretability ablation studies