Enhancing Quantum Network Establishment Through Multi-Objective Genetic Algorithm
4
Issued Date
2024-01-01
Resource Type
Scopus ID
2-s2.0-85203704706
Journal Title
Proceedings - 2024 International Conference on Quantum Communications, Networking, and Computing, QCNC 2024
Start Page
85
End Page
90
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SCOPUS
Bibliographic Citation
Proceedings - 2024 International Conference on Quantum Communications, Networking, and Computing, QCNC 2024 (2024) , 85-90
Suggested Citation
Chianvichai P., Pathumsoot P., Suwanna S. Enhancing Quantum Network Establishment Through Multi-Objective Genetic Algorithm. Proceedings - 2024 International Conference on Quantum Communications, Networking, and Computing, QCNC 2024 (2024) , 85-90. 90. doi:10.1109/QCNC62729.2024.00022 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/101270
Title
Enhancing Quantum Network Establishment Through Multi-Objective Genetic Algorithm
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Author's Affiliation
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Abstract
A quantum network connecting quantum devices is expected to enhance quantum computing and information processing by increasing the number of qubits, and distributing computation and processing. However, noise-induced decoherence remains a big challenge as a quantum network can experience multiple sources of noises. Optimizing a quantum network for a given noise regime thus can improve its performance and reliability. This research uses multi-objective optimization with a genetic algorithm to find optimal conditions for constructing a quantum network in specific configurations. We employed the qwanta quantum network simulator to investigate two scenarios of a three-node quantum network with a quantum repeater in the middle. Pareto frontier analysis enables us to visualize the trade-offs between a quantum state fidelity and throughput in a quantum network. The results provide parameters that achieve the fidelity higher than the targeted value of 0.828 for quantum key distribution in a linear network with equal distances between nodes. However, for a three-nodes network with unequal distances based on Thailand's geographical locations, the highest fidelity achieved is 0.706. Expanding a genetic algorithm's search space to include a broader parameter range can potentially improve the results. This research demonstrates potential deployment of a genetic algorithm with multiple-objective optimization in a quantum network.