13 citations · 24 across the 4 of their papers we have counts for
4 papers
Finite-size resource scaling for learning quantum phase transitions with fidelity-based support vector machines
Aaqib Ali, Giovanni Scala, Cosmo Lupo +1
Quantum kernels offer a valid procedure for learning quantum phase transitions on quantum processing devices, yet issues on the scalability of the learning strategy in connection w…
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…