OptGraphState

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, arXiv:2304.11988 [quant-ph] (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.

Installation

pip install optgraphstate

Manuals

Tutorials: https://github.com/seokhyung-lee/OptGraphState/raw/main/tutorials.pdf

API reference: https://seokhyung-lee.github.io/OptGraphState

License

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

Citation

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

@misc{lee2023graph,
      title={Graph-theoretical optimization of fusion-based graph state generation}, 
      author={Seok-Hyung Lee and Hyunseok Jeong},
      year={2023},
      eprint={2304.11988},
      archivePrefix={arXiv},
      primaryClass={quant-ph},
      url={https://arxiv.org/abs/2304.11988}
}