Graph-theoretical optimization of fusion-based graph state generation
- URL: http://arxiv.org/abs/2304.11988v4
- Date: Thu, 14 Dec 2023 03:44:12 GMT
- Title: Graph-theoretical optimization of fusion-based graph state generation
- Authors: Seok-Hyung Lee and Hyunseok Jeong
- Abstract summary: We present a graph-theoretical strategy to effectively optimize fusion-based generation of any given graph state, along with a Python package OptGraphState.
Our strategy comprises three stages: simplifying the target graph state, building a fusion network, and determining the order of fusions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Graph states are versatile resources for various quantum information
processing tasks, including measurement-based quantum computing and quantum
repeaters. Although the type-II fusion gate enables all-optical generation of
graph states by combining small graph states, its non-deterministic nature
hinders the efficient generation of large graph states. In this work, we
present a graph-theoretical strategy to effectively optimize fusion-based
generation of any given graph state, along with a Python package OptGraphState.
Our strategy comprises three stages: simplifying the target graph state,
building a fusion network, and determining the order of fusions. Utilizing this
proposed method, we evaluate the resource overheads of random graphs and
various well-known graphs. Additionally, we investigate the success probability
of graph state generation given a restricted number of available resource
states. We expect that our strategy and software will assist researchers in
developing and assessing experimentally viable schemes that use photonic graph
states.
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