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Comparison of differential evolution, particle swarm optimization, quantum-behaved particle swarm optimization, and quantum evolutionary algorithm for preparation of quantum states |
| Xin Cheng(程鑫)1,†, Xiu-Juan Lu(鲁秀娟)2,†, Ya-Nan Liu(刘亚楠)3, and Sen Kuang(匡森)1,‡ |
1 Department of Automation, University of Science and Technology of China, Hefei 230027, China;
2 Department of Mechanical Engineering, The University of Hong Kong, Hong Kong 999077, China;
3 Quantum Machines Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0495, Japan |
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Abstract Four intelligent optimization algorithms are compared by searching for control pulses to achieve the preparation of target quantum states for closed and open quantum systems, which include differential evolution (DE), particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO), and quantum evolutionary algorithm (QEA). We compare their control performance and point out their differences. By sampling and learning for uncertain quantum systems, the robustness of control pulses found by these four algorithms is also demonstrated and compared. The resulting research shows that the QPSO nearly outperforms the other three algorithms for all the performance criteria considered. This conclusion provides an important reference for solving complex quantum control problems by optimization algorithms and makes the QPSO be a powerful optimization tool.
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Received: 07 February 2022
Revised: 08 April 2022
Accepted manuscript online: 11 April 2022
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PACS:
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02.60.Pn
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(Numerical optimization)
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03.65.Aa
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(Quantum systems with finite Hilbert space)
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02.30.Yy
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(Control theory)
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02.60.Cb
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(Numerical simulation; solution of equations)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61873251). |
Corresponding Authors:
Sen Kuang
E-mail: skuang@ustc.edu.cn
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Cite this article:
Xin Cheng(程鑫), Xiu-Juan Lu(鲁秀娟), Ya-Nan Liu(刘亚楠), and Sen Kuang(匡森) Comparison of differential evolution, particle swarm optimization, quantum-behaved particle swarm optimization, and quantum evolutionary algorithm for preparation of quantum states 2023 Chin. Phys. B 32 020202
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