Establishing entangled links by entangling and swapping can enable end-to-end teleportation, avoiding eavesdropping and facilitating the critical applications, such as quantum key distribution (QKD) service. For these promising applications, a specific number of data qubits should be transmitted (periodically) to meet the quality of service (QoS) requirements for each request. Otherwise, the network provider cannot earn the profit from the request. In this paper, we present a new optimization problem and propose a novel approximation algorithm to maximize the (expected) total profit under the constraint of network resources. Finally, extensive simulation results manifest that our algorithm can outperform the existing approaches by up to 46%.
机构:
Univ Natal, Plant Cell Biol Res Unit, Dept Biol, King George 5 Ave, ZA-4001 Durban, South AfricaUniv Natal, Plant Cell Biol Res Unit, Dept Biol, King George 5 Ave, ZA-4001 Durban, South Africa
Berjak, P.
Pammenter, N. W.
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机构:
Univ Natal, Plant Cell Biol Res Unit, Dept Biol, King George 5 Ave, ZA-4001 Durban, South AfricaUniv Natal, Plant Cell Biol Res Unit, Dept Biol, King George 5 Ave, ZA-4001 Durban, South Africa
机构:
Graduate School of Information Science and Electrical Engineering, Kyushu University, JapanGraduate School of Information Science and Electrical Engineering, Kyushu University, Japan
Ihara, Takamasa
Todo, Taiki
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Faculty of Information Science and Electrical Engineering, Kyushu University, JapanGraduate School of Information Science and Electrical Engineering, Kyushu University, Japan
Todo, Taiki
Sakurai, Yuko
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机构:
Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, JapanGraduate School of Information Science and Electrical Engineering, Kyushu University, Japan
Sakurai, Yuko
Yokoo, Makoto
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Faculty of Information Science and Electrical Engineering, Kyushu University, JapanGraduate School of Information Science and Electrical Engineering, Kyushu University, Japan
Yokoo, Makoto
Transactions of the Japanese Society for Artificial Intelligence,
2017,
32
(05):