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Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

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Abstract

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61379153, 61401519, 61572927).

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Qin Liao, Ying Guo, Yifeng Tu & Hang Zhang

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  1. Qin Liao
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  2. Ying Guo
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  3. Yifeng Tu
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  4. Hang Zhang
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Correspondence to Ying Guo.

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Liao, Q., Guo, Y., Tu, Y. et al. Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System. Int J Theor Phys 57, 862–876 (2018). https://doi.org/10.1007/s10773-017-3619-9

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  • DOI: https://doi.org/10.1007/s10773-017-3619-9

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