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Journal of Physics Conference Series

A Quantum MMAS Based Algorithm for Multicast Routing Problem

Yalong Li, Wei Wang, Sun Jing, Wan Jie

Published May 1, 2021• 0 citations
Publisher

Abstract

Abstract For solving the problem of multicast routing, ant colony algorithm (ACO) has insufficient optimization ability. The paper proposes a routing algorithm which combines quantum computing and max min ACO. The algorithm uses quantum bit probability amplitude to encode the link, and dynamically adjusts quantum revolving gate to update the quantum coding on the link, so as to meet the cost optimization under constraint conditions, combined with the maximum and minimum pheromone decision mechanism to find the optimal link path, effectively control the evolution speed and direction, and enhance the convergence performance of the algorithm. The simulation results show that the algorithm can effectively control the evolution speed and direction, and is better than ant colony algorithm in the optimization ability and convergence performance.

DOI
10.1088/1742-6596/1920/1/012105

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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.
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Abstract Ant colony optimisation (ACO) is a commonly used meta‐heuristic to solve complex combinatorial optimisation problems like the travelling salesman problem (TSP), vehicle routing problem (VRP) etc. However, classical ACO algorithms provide better optimal solutions but do not reduce computation time overhead to a significant extent. Algorithmic speed‐up can be achieved by using parallelism offered by quantum computing. Existing quantum algorithms to solve ACO are either quantum‐inspired classical algorithms or hybrid quantum‐classical algorithms. Since all these algorithms need the intervention of classical computing, leveraging the true potential of quantum computing on real quantum hardware remains a challenge. This study's main contribution is to propose a fully quantum algorithm to solve ACO, enhancing the quantum information processing toolbox in the fault‐tolerant quantum computing (FTQC) era. We have solved the single source single destination (SSSD) shortest‐path problem using our proposed adaptive quantum circuit for representing the dynamic pheromone‐updating strategy in real IBMQ devices. Our quantum ACO technique can be further used as a quantum ORACLE to solve complex optimisation problems in a fully quantum setup with significant speed up upon the availability of more qubits.
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