TY - GEN
T1 - Enhancing Quantum Network Establishment Through Multi-Objective Genetic Algorithm
AU - Chianvichai, Poramat
AU - Pathumsoot, Poramet
AU - Suwanna, Sujin
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - genetic algorithm optimization
KW - quantum network
KW - quantum state fidelity
KW - throughput
UR - http://www.scopus.com/inward/record.url?scp=85203704706&partnerID=8YFLogxK
U2 - 10.1109/QCNC62729.2024.00022
DO - 10.1109/QCNC62729.2024.00022
M3 - Conference contribution
AN - SCOPUS:85203704706
T3 - Proceedings - 2024 International Conference on Quantum Communications, Networking, and Computing, QCNC 2024
SP - 85
EP - 90
BT - Proceedings - 2024 International Conference on Quantum Communications, Networking, and Computing, QCNC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Quantum Communications, Networking, and Computing, QCNC 2024
Y2 - 1 July 2024 through 3 July 2024
ER -