Quantum Annealing application towards Interpretability of Neural Networks

Item: Quantum Annealing application towards Interpretability of Neural Networks
Rating: 4
Author: PostSingularity Institute

by Francesco Aldo Venturelli

Formal & Physical Sciences Computing & Cybersecurity UComputing and Information Technology UYComputer science

🎙 Francesco Aldo Venturelli 👥 311 📅 November 28, 2025 ⏱ 24 min 👁 81 📄 original study 🧭 2026-08-16

Available in: English (current) Français

Keywords

Quantum AnnealingInterpretabilityFeature SelectionQUBOGrad-CAM

Summary

Francesco Aldo Venturelli, a PhD student at UPF and Barcelona Supercomputing Center, presents his work on using quantum annealing to improve the interpretability of convolutional neural networks (CNNs). He begins by introducing supervised learning and the black-box nature of CNNs, motivating the need for explainable AI (XAI). He explains Grad-CAM, a state-of-the-art method for generating class activation maps, but notes its limitation: it does not select the most meaningful feature maps. To address this, he formulates a feature selection problem as a QUBO (Quadratic Unconstrained Binary Optimization) problem, where the linear term maximizes positive gradient contributions and the quadratic term enforces diversity or coherence among selected feature maps. He constructs a Hamiltonian for each image and runs quantum annealing simulations. Preliminary results show that the minimum energy gap distributions are similar across different image classes, and that the selected feature maps sometimes overlap between classes, indicating potential for improvement. He plans to scale simulations and further analyze the explainability aspects. The talk concludes with a Q&A session where he clarifies the role of the strength factor and the gradient computation.

180 words

Critical Evaluation

Value of the Information & Strength of the Argument

The talk presents a novel approach to feature selection in CNNs using quantum annealing, which is a promising direction for enhancing interpretability. The argumentation is coherent, starting from the limitations of existing methods and logically deriving the QUBO formulation. The speaker provides clear explanations of the technical details, including the Hamiltonian construction and the role of the strength factor. However, the results are preliminary, and the speaker acknowledges that simulations are ongoing. The argument would be stronger with more quantitative results and comparisons to classical methods.

Scientific Rigor, Source Quality, Title Accuracy

The talk is based on original research, and the speaker references relevant literature (e.g., Grad-CAM) but does not provide specific citations. The title accurately reflects the content. The methodology is sound, but the lack of peer review and the preliminary nature of the results limit the overall rigor. The speaker is transparent about the limitations and future work.

159 words

Title / Content Match

The title accurately reflects the content, focusing on applying quantum annealing to neural network interpretability.

Quality & Reliability

7/10

The talk presents original research with a clear methodology, but results are preliminary and not yet peer-reviewed. The speaker is transparent about ongoing work and limitations.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization — Referenced as state-of-the-art method for interpretability
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks — Referenced as refinement of Grad-CAM

Concurring Sources

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization — The method the talk builds upon.

Contribution & Novelties

The talk proposes a novel application of quantum annealing to feature selection in CNNs, formulating it as a QUBO problem. This approach could enhance interpretability by selecting more meaningful feature maps. The work is original and addresses a gap in existing XAI methods.

Pour aller plus loin :

Quantum Annealing — Background on the quantum computing paradigm used.
Quadratic Unconstrained Binary Optimization — The optimization problem formulation.
Explainable Artificial Intelligence — Overview of the field.

75 words

Radar Profile

The radar profile shows a balanced performance across all dimensions, with slightly higher scores in technical level and information quality, indicating a technically sound presentation with good content depth.

Reliability 7/10