learning-ground-states-with-kernel-methods

Installation

python3 -m pip install git+https://github.com/cqsl/learning_ground_states_with_kernel_methods.git

It is advisable to install inside a virtual environment as the version of the dependencies (jax and netket) is hardcoded in the setup script. To enable the optional MPI support mpi4jax needs to be installed manually.

Simulations

The main simulation script for finding ground states is slpm/ground_state/gs.py, which is installed as slpm_gs. The documentation of its command-line arguments can be accessed with slpm_gs -h

Examples

TFI 1D

Run 100 steps of the SLPM for the TFI in 1D with 16 Spins at h=1, with a dataset of size 512, sampling from 32 chains in parallel, using Lambda=1, saving every 10 steps to /tmp/foo_*.pkl :

slpm_gs -n 100 -m tfi -d 1 -L 16 -H 1 -N 512 -c 32  -l 1 -s 10 -o /tmp/foo

where the final output will be saved in /tmp/foo_0099_rank_0_of_1.pkl.

The script can also be used to sample from the final state using a large number of samples to accurately estimate the energy. The parameters ar similar as before, except that now we only need 1 step, we take 64K samples and restart our simulations from /tmp/foo_0099_rank_0_of_1.pkl. Furthermore we tell the script to compute both the kernel matrix and estimate the energies in blocks of 256 samples, to avoid OOM.

slpm_gs -n 1 -m tfi -d 1 -L 16 -H 1 -N 65536 -c 32  -l 1 -s 10 -o /tmp/foo_final -i /tmp/foo_0099_rank_0_of_1.pkl -bsk 256 -bse 256