2 papers
quant-ph2026
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…
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…