mlsanityData to 7 Oct 2026

Quantum Gradient Class Activation Map for Model Interpretability

arXiv 2408.05899 ↗First posted 12 Aug 2024quant-ph cs.AI cs.LGcited by 9

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Abstract (arXiv)

Quantum machine learning (QML) has recently made significant advancements in various topics. Despite the successes, the safety and interpretability of QML applications have not been thoroughly investigated. This work proposes using Variational Quantum Circuits (VQCs) for activation mapping to enhance model transparency, introducing the Quantum Gradient Class Activation Map (QGrad-CAM). This hybrid quantum-classical computing framework leverages both quantum and classical strengths and gives access to the derivation of an explicit formula of feature map importance. Experimental results demonstrate significant, fine-grained, class-discriminative visual explanations generated across both image and speech datasets.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 27 of its 38 references that are in the map. First posted on arXiv 12 Aug 2024.

Builds on

27 of 38 references.

References · 27

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  4. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  5. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  6. 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 936
  7. 2014Understanding Deep Image Representations by Inverting Themcited by 399
  8. 2020The power of quantum neural networkscited by 377
  9. 2018All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneouslycited by 364
  10. 2021Generalization in quantum machine learning from few training datacited by 214
  11. 2014Object Detectors Emerge in Deep Scene CNNscited by 178
  12. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
15 more
  1. 2020Quantum Long Short-Term Memorycited by 118
  2. 2019Transfer learning in hybrid classical-quantum neural networkscited by 113
  3. 2015Visualizing Deep Convolutional Neural Networks Using Natural Pre-Imagescited by 93
  4. 2020Reinforcement Learning with Quantum Variational Circuitscited by 74
  5. 2020Quantum Convolutional Neural Networks for High Energy Physics Data Analysiscited by 64
  6. 2021The Dawn of Quantum Natural Language Processingcited by 60
  7. 2021Variational Quantum Reinforcement Learning via Evolutionary Optimizationcited by 50
  8. 2021An end-to-end trainable hybrid classical-quantum classifiercited by 49
  9. 2022Out-of-distribution generalization for learning quantum dynamicscited by 49
  10. 2022PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protectioncited by 26
  11. 2021Accuracy, Interpretability, and Differential Privacy via Explainable Boostingcited by 24
  12. 2022Quantum Multi-Agent Meta Reinforcement Learningcited by 23
  13. 2023Applying QNLP to sentiment analysis in financecited by 23
  14. 2023Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learningcited by 17
  15. 2024Feature Importance and Explainability in Quantum Machine Learningcited by 4

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Cited by year

Citing papers per year: 2025: 9.

9 papers.

  1. 2025Learning to Measure Quantum Neural Networkscited by 15
  2. 2025Adaptive Non-local Observable on Quantum Neural Networkscited by 7
  3. 2025Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generationcited by 7
  4. 2025Learning to Program Quantum Measurements for Machine Learningcited by 7
  5. 2025Transfer Learning Analysis of Variational Quantum Circuitscited by 3
  6. 2025Resting-state fMRI Analysis using Quantum Time-series Transformercited by 1
  7. 2025IQNN-CS: Interpretable Quantum Neural Network for Credit Scoringcited by 1
  8. 2025Neural Architecture Search for Quantum Autoencoderscited by 1
  9. 2025Enhancing Interpretability of Quantum-Assisted Blockchain Clustering via AI Agent-Based Qualitative Analysiscited by 0