graphstate-opt 1.1.3

Package for graph-state-optimisation

Installation

Package is available on pypi and can be intalled with pip install gso.

Optional additional requirements

MOSEK could be used for solving the ILP formulation. MOSEK requires an additional step of download and activation of license file. Mosek can be installed via pip install mosek or pip install "gso[mosek]". Information about mosek license can be found here: https://docs.mosek.com/11.0/licensing/quickstart.html.

Codes for finding the MER for a given input graph

The folder optimizer has all the files for optimisation
edm_sa.py and edm_sa_ilp.py are the two files that can be used to find the MERs. They take in: G_in: a networkx graph, k_max: maximum iterations and initial_temp: the initial temperature.

• EDM_SimAnnealing is the class for implementing simulated annealing approach for finding MERs. This is a heuristic algorithm that can go up to graphs with 100 vertices.
• edm_sa_ilp(G_in, k_max, temp) is the function for running the SA+ILP algorithm. We use it to find MERs for graphs up to 16 vertices.
• edm_ilp.py is the source code for the SA+ILP algorithm that implements the ILP part of the SA+ILP algorithm

Edge-minimisation folder

The edge minimisation folder contains the files that can be used to

• Generate bounded-degree (BD) and Erdos-Renyi (ER) graphs using gen_bd.py and gen_er.py files respectively.
• Run edge-minimisation of BD and ER graph using edm_bd_opt.py and edm_bd_opt.py files respectively.
• plot_bd.py and plot_er.py files for plotting the results for BD and ER graphs respectively.
• The files that start with grgs_sampling.py is for sampling gRGS. grgs_comparison.py is to analyse and plot the effects of varying fusion probabilities on the resources required to create a gRGS state.
• weighted_edm.py shows a use case of the weighted-edge minimization for distributing graph states in a network. It shows the input graph and the final optimized graph

The tutorial file

tutorial-1.ipynb is the file that shows how the aforementioned functions could be used to find MERs. It plots the input graph, approximate MER from SA and the exact MER from SA+ILP. It also prints the runtime of the SA+ILP and ILP algorithms.

Data files

Data files can be found at and should be put in the folder edge_minimisation so that the python files can access the data.