3 papers
cond-mat.dis-nn2026
Solving Classical and Quantum Spin Glasses with Deep Boltzmann Quantum States
Luca Leone, Arka Dutta, Markus Heyl +2
Variational neural network models have achieved remarkable success in solving ground-state problems of quantum many-body systems. However, addressing classical and quantum spin gla…
physics.comp-ph2026
Addressing the ground state of the deuteron by physics-informed neural networks
Lorenzo Brevi, Antonio Mandarino, Carlo Barbieri +1
Machine learning techniques have proven to be effective in addressing the structure of atomic nuclei. PhysicsInformed Neural Networks (PINNs) are a promising machine learning te…
quant-ph2025
Computing the molecular ground state energy in a restricted active space using quantum annealing
Stefano Bruni, Enrico Prati
Calculating the molecular ground-state energy is a central challenge in computational chemistry. Conventional methods such as the Complete Active Space Configuration Interaction sc…