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International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC)

Research on Maximizing Degree of Freedom of Arbitrary X Channel Based on Discrete Quantum Frog Leaping Algorithm

Zhengyu Dong, Feng Liu

Published Nov 7, 2025• 0 citations
Publisher

Abstract

For signal interference and DoF optimization in X channel, Discrete Quantum Frog Leaping Algorithm (DQFLA) is proposed, which combines quantum bit coding and rotating gate updating, uses quantum superposition to perform global search, and improves convergence speed through perturbation and adaptive parameter adjustment. Compared with traditional methods, this algorithm converges faster and has higher accuracy in typical X-channel simulation. It can effectively obtain global optimal solution and improve system performance.

DOI
10.1109/icairc68035.2025.11384924

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For signal interference and DoF optimization in X channel, Discrete Quantum Frog Leaping Algorithm (DQFLA) is proposed, which combines quantum bit coding and rotating gate updating, uses quantum superposition to perform global search, and improves convergence speed through perturbation and adaptive parameter adjustment. Compared with traditional methods, this algorithm converges faster and has higher accuracy in typical X-channel simulation. It can effectively obtain global optimal solution and improve system performance.
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A quantum shuffled frog leaping algorithm was proposed which combines with the quantum theory.In this algorithm,the individuals are expressed with Bloch spherical coordinates of qubits,the individual update is realized with the rotation of qubits in Bloch sphere,and the local search capabilities within the subgroup is improved with adaptive chaotic rotation angle operator.Then,to avoid premature convergence,the mutation of individuals is achieved with Hadamard gates.Above operations extend the search of the solution space effectively.Results of experiments show that compared with the SFLA,PSO and GA,the algorithm has a higher optimization capability and efficiency,and is more suitable for high-dimensional optimization of complex functions.
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In view of the shortcomings of particle swarm optimization such as premature convergence to local optimization, particle swarm optimization algorithm based on quantum gate and particle swarm optimization algorithm based on quantum behavior are studied in this paper. The first algorithm uses the random observation method of quantum bit coding particles to simulate quantum particle collapse for generating a population and uses the quantum rotating gate to generate a new population. The adaptive mutation operator is used for ensuring the diversity of the population, effectively reducing the impact of local optimization; therefore, the robustness of the algorithm is improved. The second algorithm uses the probability density function of quantum computation to make the particles jump out of the local extreme points and fulfils the global search, which is more suitable for continuous optimization. The results of computational experiments show that both the two particle swarm optimization algorithms based on quantum technology have a good global convergence.