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…
nucl-th2025
Quasiparticle pairing encoding of atomic nuclei for quantum annealing
Emanuele Costa, Axel Pérez-Obiol, Javier Menéndez +3
Quantum computing is emerging as a promising tool in nuclear physics. However, the cost of encoding fermionic operators hampers the application of algorithms in current noisy quant…
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…