GPT is a Python measurement toolkit built on Grid data parallelism (MPI, OpenMP, SIMD, and SIMT). It provides a physics library for lattice QCD and related theories as well as a QIS module including a digital quantum computing simulator.
The fastest way to try GPT is to install Docker, start a Jupyter notebook server with the latest GPT version by running
docker run --rm -p 8888:8888 gptdev/notebook
and then open the shown link http://127.0.0.1:8888/?token=<token> in a browser.
You should see the tutorials folder pre-installed.
Note that this session does not retain data after termination. Run
docker run --rm -p 8888:8888 -v $(pwd):/notebooks gptdev/notebook
to instead mount the current working directory on your machine.
Please consult the GPT Docker documentation for additional options.
A detailed description on how to install GPT locally can be found here.
You may also visit a static version of the tutorials here.
import gpt as g
# load gauge field and describe fermion
gauge=g.qcd.gaugefield("params.txt")
light=g.qcd.fermion("light.txt")
# create point source
src=g.mspincolor(gauge.dp.grid)
g.create.point(src, [0,0,0,0])
# solve
prop=light.solve.exact(src)
# pion
corr_pion=g.slice(g.trace(g.adj(prop)*prop),3)
print("Pion two point:")
print(corr_pion)
# vector
gamma=g.gamma
corr_vector=g.slice(g.trace(gamma[0]*g.adj(prop)*gamma[0]*prop),3)
print("Vector two point:")
print(corr_vector)