Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Research on Multi-hop Transmission in Quantum Wireless Communication Networks Based on Improved Ant Colony Algorithm

Xinyuan Mao, Min Nie, Guang Yang

Published Sep 23, 2022• 0 citations
Publisher

Abstract

Firstly, an improved ant colony algorithm (QCANT) is proposed to optimize quantum connectivity, and the entanglement example distribution node deployment in quantum wireless multi-hop networks is studied and analyzed. On this basis, this paper combined genetic algorithm with improved ant colony algorithm (GA-QCANT), which can effectively alleviate the problem of low efficiency of ant colony algorithm due to the lack of initial pheromone. Simulation results show that both QCANT and GA-QCANT improves quantum connectivity significantly, and GA-QCANT improves quantum connectivity by an average of 32.1% compared to QCANT.

DOI
10.1145/3573942.3573985

Related papers

Quantum connectivity optimization algorithms for entanglement source deployment in a quantum multi-hop network

Zhen-Zhen Zou, Xutao Yu, Zaichen Zhang

At first, the entanglement source deployment problem is studied in a quantum multi-hop network, which has a significant influence on quantum connectivity. Two optimization algorithms are introduced with limited entanglement sources in this paper. A deployment algorithm based on node position (DNP) improves connectivity by guaranteeing that all overlapping areas of the distribution ranges of the entanglement sources contain nodes. In addition, a deployment algorithm based on an improved genetic algorithm (DIGA) is implemented by dividing the region into grids. From the simulation results, DNP and DIGA improve quantum connectivity by 213.73% and 248.83% compared to random deployment, respectively, and the latter performs better in terms of connectivity. However, DNP is more flexible and adaptive to change, as it stops running when all nodes are covered.

Frontiers of Physics•Sep 29, 2017•13 citations

Research on quantum satellite access selection based on Q-learning quantum ant colony algorithm

Yan Kang, Min Nie, Guang Yang

The development and deployment of low-orbit quantum satellites has become one of the important research directions in the field of quantum communication technology. In order to improve the security and service quality of quantum satellite network, this paper proposes a multi-attribute quantum satellite selection access technique based on Q-learning quantum ant colony. Considering the two optimization objectives of path combined trust value and path cost, the overall trust function is defined according to the direct trust value and indirect trust value to improve the security, and the cost function is defined according to the channel QBER(Quantum bit error rate), interstellar transmission delay, and channel establishment rate to improve the service quality. Simulation results show that compared with the traditional ant colony optimization algorithm and the improved quantum ant colony multi-constraint algorithm, the Q-learning quantum ant colony algorithm significantly improves the performance indexes such as the packet delivery rate and the system blocking rate of the quantum satellite access selection, and improves the convergence speed, which is of certain reference significance for the research on the access selection of quantum satellites.

•Sep 20, 2024•0 citations

Optimization Strategy for Satelliet Submarine Quantum Communication based on Improved Ant Colony Algorithm

Bowei Lei, Min Nie, Guang Yang

This article proposes an optimization strategy for star submarine quantum communication based on improved ant colony algorithm, aiming to solve the problems of signal attenuation and bit error rate caused by factors such as particle pollution and transmission distance in the star submarine quantum communication link. By introducing an improved ant colony algorithm, key parameters in quantum communication links (bit error rate, link attenuation, and satellite signal strength) were optimized, and a comprehensive performance objective function was constructed based on the characteristics of quantum communication. The experimental results show that the improved ant colony algorithm can effectively improve the receiving efficiency of quantum optical signals, reduce the bit error rate, and maintain high communication reliability in complex environments. Compared with traditional ant colony algorithm and three multi-objective optimization algorithms, the improved ant colony algorithm shows significant advantages in objective function correlation, time complexity, and actual communication performance. This study provides new ideas for the combination of star submarine quantum communication and artificial intelligence fields.

International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)•Jul 26, 2025•0 citations

Quantum ant colony algorithm for optimization of logistics distribution route

Shen Pen

The logistics distribution route problem is a NP problem which possesses important practical value. A novel optimization method of logistics distribution route is proposed based on Quantum Ant Colony Algorithm(QACA)to overcome the problems such as long computing time and easy to fall into local best for traditional heuristic optimization algorithm. The optimization problem of logistics distribution routing is analyzed, and the mathematical model is established, and then the quantum ant colony algorithm is used to solve it, and the pheromone on each path is encoded by a group of quantum bits,and a new pheromone representation is designed,called quantum pheromone, and the quantum rotation gate and the best tour are applied to updating the pheromone. The simulation test is carried out to test the performance of QACA. The simulation results show that, QACA has a strong global search ability and convergence speed, and can effectively solve the logistics distribution routing problem.

Computer Engineering and Applications Journal•Jan 1, 2013•1 citation

Novel quantum ant colony algorithm for TSP

Fuxiao Tan

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.

Computer Engineering and Applications Journal•Jan 1, 2011•1 citation

Improved quantum ant colony algorithm for TSP

Yuanping Zhang

The algorithm is based on the combination of quantum evolutionary algorithm(QEA) and ant colony system(ACS),a new algorithm,quantum ant colony algorithm(QACA) is proposed.The core is that Q-bit and quantum rotation gate adopted in QEA are introduced into ACS to represent and update the pheromone respectively,so it has better diversity and global search capacity.The ex-perimental result demonstrates that QACA can get better solutions to some traveling salesman problems(TSP) than the solutions given in existing bibliographer,it indicate that the algorithm is effective to solve TSP.

Jisuanji gongcheng yu sheji•Jan 1, 2009•4 citations
Firstly, an improved ant colony algorithm (QCANT) is proposed to optimize quantum connectivity, and the entanglement example distribution node deployment in quantum wireless multi-hop networks is studied and analyzed. On this basis, this paper combined genetic algorithm with improved ant colony algorithm (GA-QCANT), which can effectively alleviate the problem of low efficiency of ant colony algorithm due to the lack of initial pheromone. Simulation results show that both QCANT and GA-QCANT improves quantum connectivity significantly, and GA-QCANT improves quantum connectivity by an average of 32.1% compared to QCANT.
At first, the entanglement source deployment problem is studied in a quantum multi-hop network, which has a significant influence on quantum connectivity. Two optimization algorithms are introduced with limited entanglement sources in this paper. A deployment algorithm based on node position (DNP) improves connectivity by guaranteeing that all overlapping areas of the distribution ranges of the entanglement sources contain nodes. In addition, a deployment algorithm based on an improved genetic algorithm (DIGA) is implemented by dividing the region into grids. From the simulation results, DNP and DIGA improve quantum connectivity by 213.73% and 248.83% compared to random deployment, respectively, and the latter performs better in terms of connectivity. However, DNP is more flexible and adaptive to change, as it stops running when all nodes are covered.
The development and deployment of low-orbit quantum satellites has become one of the important research directions in the field of quantum communication technology. In order to improve the security and service quality of quantum satellite network, this paper proposes a multi-attribute quantum satellite selection access technique based on Q-learning quantum ant colony. Considering the two optimization objectives of path combined trust value and path cost, the overall trust function is defined according to the direct trust value and indirect trust value to improve the security, and the cost function is defined according to the channel QBER(Quantum bit error rate), interstellar transmission delay, and channel establishment rate to improve the service quality. Simulation results show that compared with the traditional ant colony optimization algorithm and the improved quantum ant colony multi-constraint algorithm, the Q-learning quantum ant colony algorithm significantly improves the performance indexes such as the packet delivery rate and the system blocking rate of the quantum satellite access selection, and improves the convergence speed, which is of certain reference significance for the research on the access selection of quantum satellites.
This article proposes an optimization strategy for star submarine quantum communication based on improved ant colony algorithm, aiming to solve the problems of signal attenuation and bit error rate caused by factors such as particle pollution and transmission distance in the star submarine quantum communication link. By introducing an improved ant colony algorithm, key parameters in quantum communication links (bit error rate, link attenuation, and satellite signal strength) were optimized, and a comprehensive performance objective function was constructed based on the characteristics of quantum communication. The experimental results show that the improved ant colony algorithm can effectively improve the receiving efficiency of quantum optical signals, reduce the bit error rate, and maintain high communication reliability in complex environments. Compared with traditional ant colony algorithm and three multi-objective optimization algorithms, the improved ant colony algorithm shows significant advantages in objective function correlation, time complexity, and actual communication performance. This study provides new ideas for the combination of star submarine quantum communication and artificial intelligence fields.
The logistics distribution route problem is a NP problem which possesses important practical value. A novel optimization method of logistics distribution route is proposed based on Quantum Ant Colony Algorithm(QACA)to overcome the problems such as long computing time and easy to fall into local best for traditional heuristic optimization algorithm. The optimization problem of logistics distribution routing is analyzed, and the mathematical model is established, and then the quantum ant colony algorithm is used to solve it, and the pheromone on each path is encoded by a group of quantum bits,and a new pheromone representation is designed,called quantum pheromone, and the quantum rotation gate and the best tour are applied to updating the pheromone. The simulation test is carried out to test the performance of QACA. The simulation results show that, QACA has a strong global search ability and convergence speed, and can effectively solve the logistics distribution routing 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.
The algorithm is based on the combination of quantum evolutionary algorithm(QEA) and ant colony system(ACS),a new algorithm,quantum ant colony algorithm(QACA) is proposed.The core is that Q-bit and quantum rotation gate adopted in QEA are introduced into ACS to represent and update the pheromone respectively,so it has better diversity and global search capacity.The ex-perimental result demonstrates that QACA can get better solutions to some traveling salesman problems(TSP) than the solutions given in existing bibliographer,it indicate that the algorithm is effective to solve TSP.