https://github.com/lehner/gpt

A Python toolkit for lattice field theory, quantum computing, and machine learning
https://github.com/lehner/gpt

gpt grid lattice-field-theory lattice-gauge-theory lattice-qcd

Last synced: 3 months ago
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A Python toolkit for lattice field theory, quantum computing, and machine learning

• Host: GitHub
• URL: https://github.com/lehner/gpt
• Owner: lehner
• License: gpl-2.0
• Created: 2020-02-23T19:50:58.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-07-26T17:48:34.000Z (4 months ago)
• Last Synced: 2024-07-26T19:31:33.296Z (4 months ago)
• Topics: gpt, grid, lattice-field-theory, lattice-gauge-theory, lattice-qcd
• Language: Python
• Homepage:
• Size: 5.16 MB
• Stars: 92
• Watchers: 15
• Forks: 37
• Open Issues: 16
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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![GPT Logo](/documentation/logo/logo-1280-640.png)

# GPT - Grid Python Toolkit

GPT is a [Python](https://www.python.org) measurement toolkit built on [Grid](https://github.com/paboyle/Grid) data parallelism (MPI, OpenMP, SIMD, and SIMT).

It provides a physics library for lattice QCD and related theories, a QIS module including a digital quantum computing simulator, and a machine learning module.

## Quick Start

The fastest way to try GPT is to install [Docker](https://docs.docker.com/get-docker/),

start a [Jupyter](https://jupyter.org/) 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=` 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](https://github.com/lehner/gpt/tree/master/docker/README.md) for additional options.

## Installation

A detailed description on how to install GPT

locally can be found [here](README.setup.md).

## Tutorials

You may also visit a static version of the tutorials [here](https://github.com/lehner/gpt/tree/master/documentation/tutorials).

## Usage

```python

import gpt as g

# Double-precision 8^4 grid

grid = g.grid([8,8,8,8], g.double)

# Parallel random number generator

rng = g.random("seed text")

# Random gauge field

U = g.qcd.gauge.random(grid, rng)

# Mobius domain-wall fermion

fermion = g.qcd.fermion.mobius(U, mass=0.1, M5=1.8, b=1.0, c=0.0, Ls=24,

                               boundary_phases=[1,1,1,-1])

# Short-cuts

inv = g.algorithms.inverter

pc = g.qcd.fermion.preconditioner

# Even-odd-preconditioned CG solver

slv_5d = inv.preconditioned(pc.eo2_ne(), inv.cg(eps = 1e-4, maxiter = 1000))

# Abstract fermion propagator using this solver

fermion_propagator = fermion.propagator(slv_5d)

# Create point source

src = g.mspincolor(U[0].grid)

g.create.point(src, [0, 0, 0, 0])

# Solve propagator on 12 spin-color components

prop = g( fermion_propagator * src )

# Pion correlator

g.message(g.slice(g.trace(prop * g.adj(prop)), 3))

```