دانلود رایگان مقاله انگلیسی الگوریتم کلونی مورچه بر اساس صلاحیت با Q-learning سیستم کوانتومی - اشپرینگر 2018

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.

6 Conclusion

In this paper, a fidelity-based ACA is presented for the control design of quantum system. To improve the performance of fidelity-based ACA, a fidelity-based ACA with Q-learning is introduced. In this improved algorithm, the fidelity information can be extracted from the system structure or the system behavior. The aim is to design a good exploration strategy for a better tradeoff between exploration and exploitation, and to speed up the convergence as well. Experimental results show that fidelity-based ACA with Q-learning is superior to the fidelity-based ACA. The control problems of a spin-(1/2) system is adopted to demonstrate the performance of the fidelity-based ACA with Q-learning. In the future, our work will focus on improving the fidelity-based ACA by combining with other algorithms.