3 papers
cs.LG2026
Towards interpretable AI with quantum annealing feature selection
Francesco Aldo Venturelli, Emanuele Costa, Sikha O K +3
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 predicti…
quant-ph2026
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem
Francesco Aldo Venturelli, Sreetama Das, Filippo Caruso
The quantum approximate optimization algorithm (QAOA) is a variational quantum algorithm (VQA) ideal for noisy intermediate-scale quantum (NISQ) processors, and is highly successfu…
quant-ph2025
Physics-inspired Generative AI models via real hardware-based noisy quantum diffusion
Marco Parigi, Stefano Martina, Francesco Aldo Venturelli +1
Quantum Diffusion Models (QDMs) are an emerging paradigm in Generative AI that aims to use quantum properties to improve the performances of their classical counterparts. However,…