5 papers
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…
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…
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…
A Tutorial on the Use of Physics-Informed Neural Networks to Compute the Spectrum of Quantum Systems
Lorenzo Brevi, Antonio Mandarino, Enrico Prati
Quantum many-body systems are of great interest for many research areas, including physics, biology and chemistry. However, their simulation is extremely challenging, due to the ex…
Addressing the Non-perturbative Regime of the Quantum Anharmonic Oscillator by Physics-Informed Neural Networks
Lorenzo Brevi, Antonio Mandarino, Enrico Prati
The use of deep learning in physical sciences has recently boosted the ability of researchers to tackle physical systems where little or no analytical insight is available. Recentl…