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International Conference on Smart Grid and Electrical Automation (ICSGEA)

Fidelity-Based Ant Colony Optimization for Control of Quantum System

Hang Zhang, Yifeng Tu, Ying Guo

Published May 1, 2017• 1 citation
Publisher

Abstract

The search time of traditional ant colony Algorithm (ACA) is too long, and it tends to trap in local optimizations. In this paper, a fidelity-based ACA with Q-learning is presented to solve above-mentioned problem and applied for control of quantum system. In the fidelity-based ACA with Q-learning, fidelity is used in heuristic function, which can help the system converge to the optimal solution. And the fidelity-based ACA with Q-learning can simplify the parameter setting of ACA algorithm. An example (a spin-1/2 system) is demonstrated to test the performance of the fidelity-based ACA with Q-learning. The results show that the fidelity-based ACA with Q-learning can efficiently avoid trapping in local optimal policies and increase the speed of convergence process.

DOI
10.1109/icsgea.2017.23

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Aiming at the shortcoming of ant colony optimization of being only suitable for discrete problems and holding a slow convergence speed, a novel algorithm for continuous optimization problems is presented. In this algorithm, each ant carries a group of qubits which represents the position of its own. First, the destination to which ant want to move is selected according to the select probability based on pheromone information and heuristic information. Then, the ant's own qubits are updated by quantum rotation gates so as to move. Some ants' qubits are mutated by quantum non-gate so as to increase the diversity of positions. Finally, both information of the pheromone and the heuristic are updated according to the new position of each ant. In this algorithm, both probability amplitudes of a qubit are regarded as position information, a double searching efficiency is acquired for ant colony which hold the fixed number of ants. The availability of the proposed algorithm is illustrated by simulation examples of function extremum optimization.
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A quantum-inspired ant colony algorithm(QACA) for the classic graph coloring problem is proposed based upon the combination of ant colony optimization and quantum computing.The quantum bits and quantum logic gates are introduced to the ant colony algorithm(ACA),and the disadvantage of getting into the local optimum can be effectively avoided.The computational speed of the algorithm is therefore significantly improved.Series of simulation instances of the graph coloring problem are tested.And the results show that the QACA is a feasible,effective and versatile method for solving the graph coloring problem.
Ant Colony Algorithm(ACA) demonstrates the superiority in solving TSP,and Quantum Evolution Algorithm (QEA) has the performance of high efficiency on combinational optimization problems,so combining the thought of ACA with QEA,a novel quantum ant colony algorithm for TSP is proposed.In this algorithm,the pheromone on each path is encoded by a group of quantum bits,and a new pheromone representation is designed,called quantum pheromone.The quantum rotation gate and the best tour are applied to update the pheromone so as to accelerate its convergence speed.To avoid the search falling into local optimum,the strategy of quantum crossover is designed to improve the information structure of population.Simulation results show that the algorithm has fast convergence speed and global optimal ability,and the algorithm is more effective than ACS.