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-ph2025
Adaptive-basis sample-based neural diagonalization for quantum many-body systems
Simone Cantori, Luca Brodoloni, Edoardo Recchi +3
Accurately estimating ground-state energies of quantum many-body systems is still a challenging computational task because of the exponential growth of the Hilbert space with the s…
physics.comp-ph2024
Solving deep-learning density functional theory via variational autoencoders
Emanuele Costa, Giuseppe Scriva, Sebastiano Pilati
In recent years, machine learning models, chiefly deep neural networks, have revealed suited to learn accurate energy-density functionals from data. However, problematic instabilit…