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 - Continuous-time quantum walk analysis framework.

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 2023.1008a0

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 2023.1008a0
File Size Uploaded
qwak-sim-2023.1008a0.tar.gz 61.2 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for qwak-sim 2023.1008a0
File Interpreter ABI Platform
qwak_sim-2023.1008a0-py3-none-any.whl Python 3 none any Details

Total release size: 115.0 kB

Release files / qwak-sim-2023.1008a0.tar.gz

Download URL qwak-sim-2023.1008a0.tar.gz
Size 61.2 kB
Tags Source
SHA-256 checksum
How to use checksums
f1266c816704a79e61800e6a09ed0b9458e781a8925697545304c560586ec090
BLAKE2b-256 checksum
How to use checksums
570e49c0cb66c9e9451e38d59c624c0011d0d86bef98a2f463a23420b22fd5ee
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.10.4

Release files / qwak_sim-2023.1008a0-py3-none-any.whl

Download URL qwak_sim-2023.1008a0-py3-none-any.whl
Size 53.8 kB
Tags Python 3
SHA-256 checksum
How to use checksums
d20b8e33dcf62b1ee13359133208d248d2505fb80c0d11d1e33bfbbb513d6e10
BLAKE2b-256 checksum
How to use checksums
809aa026410b6fc3de2f52a4de1724ac6fc3ed1c5a9f01af39a50d3f9164c6c0
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.10.4
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