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Pre-release

This release is a pre-release and may not be stable for production use.

QWAK

Quantum Walk Analysis Kit - A Python package for continuous-time quantum walk (CTQW) simulation.

Additionally, a fullstack web app built with Flask and PyMongo is available on Heroku.

This package supports python 3.10 or newer.

Table of Contents:

Funding

This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDB/50014/2020. The project was also partially supported by Next Generation EU, in the context of the National Recovery and Resilience Plan, M4C2 investment 1.2. Project SOE0000098-ThermoQT.

Installation

You can install the package both through PyPi pip install qwak-sim or locally, by cloning the project, installing the requirements via pip followed by pip install . in the cloned folder. A virtual environment is highly recommended.

Dependencies

  • Numpy
  • Scipy
  • Sympy
  • matplotlib
  • networkx
  • QuTip
  • eel

Step-by-step installation instructions can be found in the documentation installation page.

Usage

A basic plot of the probability distribution for a CTQW with a walker starting in a superposition of central positions, in a cyclic graph, can be achieved via the following example:

import networkx as nx
import matplotlib.pyplot as plt
from qwak.qwak import QWAK

n = 100
t = 12
initState = [n//2,n//2 + 1]
graph = nx.cycle_graph(n)

qwak = QWAK(graph)
qwak.runWalk(t,initState)

probVec = qwak.getProbVec()
plt.plot(probVec)
plt.show()

Further examples exploring all the different components will be available once the usage documentation is complete.

Documentation

Documentation is a work in progress, and can be found in this page.

Contributing

Extra requirements

  • autopep8
  • pytest
  • sphinx

Contributing to the package follows a relatively simple workflow. After performing the necessary setup procedures, you will update your fork with the latest version of the QWAK project. You can now perform your changes, format them and test them. If a new feature is added, you will need to add docstrings to the new methods and update the existing documentation accordingly. If your contribution is directly to the documentation, you will follow a similar procedure.

Step-by-step instructions on how to setup all the required components for organized contribution can be found on the contributing documentation page.

Metadata

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