Version 0.3.1

Graph-theoretical optimization of fusion-based graph state generation.

OptGraphState is a python package that implements the graph-theoretical strategy to optimize the fusion-based generation of any graph state, which is proposed in Lee & Jeong, Quantum 7, 1212 (2023).

The package has the following features:

• Finding a resource-efficient method of generating a given graph state through type-II fusions from multiple basic resource states, which are three-qubit linear graph states.
• Computing the corresponding resource overhead, which is quantified by the average number of required basic resource states or fusion attempts.
• Computing the success probability of graph state generation when the number of provided basic resource states is limited.
• Visualizing the original graph (of the graph state you want to generate), unraveled graphs, and fusion networks. An unraveled graph is a simplified graph where the corresponding graph state is equivalent to the desired graph state up to fusions and single-qubit Clifford operations. A fusion network is a graph that instructs the fusions between basic resource states required to generate the desired graph state.
• Various predefined sample graphs for input.

The specified versions of these requirements are the ones with which we have tested OptGraphState. It is highly probable that newer versions work equally well.

(Exceptionally, we have checked that the latest version of python-igraph raises an error when visualizing graphs due to some internal changes of the library related to plotting graphs. Other functions may work well.)

• python == 3.9
• numpy == 1.24.2
• python-igraph == 0.10.4
• networkx == 3.1
• matplotlib == 3.7.1
• parmap == 1.6.0 (Optional, for multiprocessing)
• tqdm == 4.66.1 (Optional, for progress bar in multiprocessing)

pip install optgraphstate

• Tutorials
• API reference

OptGraphState is distributed under the MIT license. Please see the LICENSE file for more details.

If you want to cite OptGraphState in an academic work, please cite the paper:

@article{lee2023graph,
  doi = {10.22331/q-2023-12-20-1212},
  title = {Graph-theoretical optimization of fusion-based graph state generation},
  author = {Lee, Seok-Hyung and Jeong, Hyunseok},
  journal = {{Quantum}},
  issn = {2521-327X},
  publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
  volume = {7},
  pages = {1212},
  month = dec,
  year = {2023}
}