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Quantum Network Coding Based on Entanglement Distribution

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11635))

Abstract

Quantum network coding (QNC) is a new technology of quantum network communication. Since QNC can maximize the communication efficiency of quantum communication networks, it receives wide attention. As an important quantum communication resource, quantum entanglement plays a key role in the field of quantum communication and quantum computation, of course, including QNC. Several typical QNC schemes require quantum entanglement to achieve lossless quantum communication. However, none of these previous schemes mentioned the formation and distribution of quantum entanglement. Moreover, the entangled resources required by these schemes are more demanding and the required experimental environment is harsh, which is difficult to operate in practice. Therefore, with the help of entanglement distribution by separable states and probabilistic cloning, we propose a novel quantum network coding scheme based on entanglement distribution. This scheme can successfully achieve quantum entanglement distribution in the butterfly network. It is efficient in the use of quantum resources and has stronger resistance to environmental noise and other disturbances. We also point out that quantum discord, as a more general quantum communication resource, controls the realization of the whole communication process.

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Acknowledgment

This project was supported by the National Natural Science Foundation of China (No. 61571024) for valuable helps.

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Correspondence to Tao Shang .

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Shang, T., Liu, R., Fang, C., Liu, J. (2019). Quantum Network Coding Based on Entanglement Distribution. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11635. Springer, Cham. https://doi.org/10.1007/978-3-030-24268-8_2

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  • DOI: https://doi.org/10.1007/978-3-030-24268-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24267-1

  • Online ISBN: 978-3-030-24268-8

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