Abstract Quantum networks describe communication networks that are based on quantum entanglement. A concurrence percolation theory has been recently developed to determine the required entanglement to enable communication between two distant stations in an arbitrary quantum network. Unfortunately, concurrence percolation has been calculated only for very small networks or large networks without loops. Here, we develop a set of mathematical tools for approximating the concurrence percolation threshold for unprecedented large-scale quantum networks by estimating the path-length distribution, under the assumption that all paths between a given pair of nodes have no overlap. We show that our approximate method agrees closely with analytical results from concurrence percolation theory. The numerical results we present include 2D square lattices of 2002nodes and complex networks of up to 104nodes. The entanglement percolation threshold of a quantum network is a crucial parameter for constructing a real-world communication network based on entanglement, and our method offers a significant speed-up for the intensive computations involved.
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THE DETERMINATION OF A 3D LENGTH SCALE USING LONG RECONSTRUCTED FIBER PATHS
Carbon fiber reinforced composites often exhibit large amounts of property scatter. Attempts at understanding composite property scatter have led researchers to generate many 2D models which ignore the 3D phenomenon of entanglement. Previous studies of entanglement have suggested it is correlated to a length scale, but have not had large enough samples to determine its size. In this study, fiber paths of long, entangled, continuous fibers were extracted from CT data of an automotive grade, heavy tow composite. Descriptive metrics of these fiber paths were used to quantify the entanglement as a function of position along the fiber direction. Using this data, several minimum length scales for capturing the behavior of multiple descriptors were determined. These length scales revealed where statistical representation of 3D fiber models provides superior information to that of 2D models.
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- Award ID(s):
- 1916715
- PAR ID:
- 10489470
- Publisher / Repository:
- Destech Publications, Inc.
- Date Published:
- ISBN:
- 9781605956909
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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