Trace-Distance based End-to-End Entanglement Fidelity with Information Preservation in Quantum Networks

• URL: http://arxiv.org/abs/2410.15603v1
• Date: Mon, 21 Oct 2024 03:02:26 GMT
• Title: Trace-Distance based End-to-End Entanglement Fidelity with Information Preservation in Quantum Networks
• Authors: Pankaj Kumar, Binayak Kar, Shan-Hsiang Shen,
• Abstract summary: entanglement leads to an exponential decrease in fidelity as the distance between quantum nodes increases. This phenomenon makes it challenging to generate high-fidelity entangled pairs and preserve information in quantum networks. We propose two strategies to ensure high-fidelity entangled pairs and information preservation within a quantum network.
• Score: 2.173168795387279
• License:
• Abstract: Quantum networks hold the potential to revolutionize a variety of fields by surpassing the capabilities of their classical counterparts. Many of these applications necessitate the sharing of high-fidelity entangled pairs among communicating parties. However, the inherent nature of entanglement leads to an exponential decrease in fidelity as the distance between quantum nodes increases. This phenomenon makes it challenging to generate high-fidelity entangled pairs and preserve information in quantum networks. To tackle this problem, we utilized two strategies to ensure high-fidelity entangled pairs and information preservation within a quantum network. First, we use closeness centrality as a metric to identify the closest nodes in the network. Second, we introduced the trace-distance based path purification (TDPP) algorithm, specifically designed to enable information preservation and path purification entanglement routing. This algorithm identifies the shortest path within quantum networks using closeness centrality and integrates trace-distance computations for distinguishing quantum states and maintaining end-to-end (E2E) entanglement fidelity. Simulation results demonstrate that the proposed algorithm improves network throughput and E2E fidelity while preserving information compared to existing methods.

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