A collection of functions for multipartite entanglement purification protocols (EPP) on noisy graph states
Project description
GraphEPP
GraphEPP is a collection of functions for multipartite entanglement purification protocols (EPP) on noisy graph states.
Installation
You can install GraphEPP into your python environment from the Python Package Index:
pip install graphepp
If you encounter any problems, you can try installing it the exact versions of
GraphEPP's dependencies that were used to develop it
(specified in Pipfile.lock). This assumes Python 3.8 and
pipenv are installed on your system:
git clone https://github.com/jwallnoefer/graphepp.git
cd graphepp
git checkout main
pipenv sync
pipenv install graphepp
and then use pipenv shell to activate the virtual environment
Scope
This project provides functions for operations on quantum states that are diagonal in the graph state basis corresponding to a chosen graph state. (for an introduction to graph states see e.g. arXiv:quant-ph/0602096) These include:
- Local Pauli-diagonal noise channels that are applied on qubits of the graph state.
- Distance measures for states given in the same graph state basis.
- The ADB protocol for two-colorable graph states introduced in Phys. Rev. Lett. 91, 107903 (2003) and Phys. Rev. A 71, 012319 (2005).
- The protocol for all graph states introduced in Phys. Rev. A 74, 052316 (2006).
as well as auxiliary functions like local complementation of graphs.
Example use case: Perform multiple rounds of EPP on a noisy linear cluster state of 5 qubits when the CNOT operations used in the EPP are themselves imperfect (e.g. modeled by local depolarizing noise).
Publications
An earlier (unreleased) version of GraphEPP was used for these publications:
Two-dimensional quantum repeaters
J. Wallnöfer, M. Zwerger, C. Muschik, N. Sangouard, and W. Dür
Phys. Rev. A 94, 052307 (2016)
Preprint: arXiv:1604.05352 [quant-ph]
Measurement-based quantum communication with resource states generated by entanglement purification
J. Wallnöfer and W. Dür
Phys. Rev. A 95, 012303 (2017)
Preprint: arXiv:1609.05754 [quant-ph]
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file graphepp-0.5.tar.gz.
File metadata
- Download URL: graphepp-0.5.tar.gz
- Upload date:
- Size: 47.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.12.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
b2c74e842fe81b27f62e612d53b1a6afd5eef8b187598bb5bdc718dbdfecfe26
|
|
| MD5 |
dee185c44d475ae99543880e9fac275b
|
|
| BLAKE2b-256 |
c7eaa1a4bb17b2437d8ce1a836020efaccaa6f2267954a3338c7124e76ff7377
|
File details
Details for the file graphepp-0.5-py3-none-any.whl.
File metadata
- Download URL: graphepp-0.5-py3-none-any.whl
- Upload date:
- Size: 11.3 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.12.5
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
706bb2850a974fb9d46d05228924fcd0771d49fe2da08cd6e5cc31f0151bec48
|
|
| MD5 |
8538115746349cfccdc591117581fbdb
|
|
| BLAKE2b-256 |
235443990daa9bb032a950887d66d1109282ea837a84f120bb43fc0454d98dc5
|