Learning ground states of gapped quantum Hamiltonians with Kernel Methods

no code implementations • 15 Mar 2023 • Clemens Giuliani, Filippo Vicentini, Riccardo Rossi, Giuseppe Carleo

Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem.

NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

1 code implementation • 20 Dec 2021 • Filippo Vicentini, Damian Hofmann, Attila Szabó, Dian Wu, Christopher Roth, Clemens Giuliani, Gabriel Pescia, Jannes Nys, Vladimir Vargas-Calderon, Nikita Astrakhantsev, Giuseppe Carleo

We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics.

BIG-bench Machine Learning Chunking +2