Towards interpretable AI with quantum annealing feature selection
Deep learning models are used in critical applications, in which mistakes can have serious consequences. Therefore, it is crucial to understand how and why models generate predictions. This understanding provides useful information to check whether the model is learning the right patterns, detect biases in the data, improve model design, and build systems that can be trusted. This work proposes a new method for interpreting Convolutional Neural Networks in image classification tasks. The approach works by selecting the most representative feature maps that contribute to each prediction. To solve this combinatorial problem, we encode it into a quantum constrained optimization problem and p...
| Authors | Francesco Aldo Venturelli, Emanuele Costa, Sikha O K et al. |
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| Primary category | cs.LG |
| Published | 28 April 2026 |
| Last updated at source | 2 June 2026 |
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