13 citations · 24 across the 4 of their papers we have counts for
4 papers
Efficient simulation of noisy entanglement generation
Lorenzo Brevi, Federico Grasselli, Alessandro Caraceni +3
End-to-end entanglement distribution is a key capability of upcoming quantum networks, enabling applications like distributed quantum computing, quantum sensor networks, and secure…
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…
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…