Skip to main content
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.

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.

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

Release files for qwak-sim 2023.1014a0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for qwak-sim 2023.1014a0
File Size Uploaded
qwak-sim-2023.1014a0.tar.gz 71.1 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for qwak-sim 2023.1014a0
File Interpreter ABI Platform
qwak_sim-2023.1014a0-py3-none-any.whl Python 3 none any Details

Total release size: 130.5 kB

Release files / qwak-sim-2023.1014a0.tar.gz

Download URL qwak-sim-2023.1014a0.tar.gz
Size 71.1 kB
Tags Source
SHA-256 checksum
How to use checksums
5a15c1726d6d2c1acab944c2dac56ca686f849c5136e0ab8fd8073e04572c76f
BLAKE2b-256 checksum
How to use checksums
459a7c594926191174878360143eea9223832583f0f464f2e7db81acb38c4321
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.10.9

Release files / qwak_sim-2023.1014a0-py3-none-any.whl

Download URL qwak_sim-2023.1014a0-py3-none-any.whl
Size 59.3 kB
Tags Python 3
SHA-256 checksum
How to use checksums
706084370f902225fc008dd68a43a13a000ad4405248b5084c016e648d427da6
BLAKE2b-256 checksum
How to use checksums
406cd404e5f9513106490725b7ae2d0969047969b36e57cd29aa6adcc0fd8874
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.10.9
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page