A Genetic Algorithm with Quantum Circuit Objective Function for Optimizing Communication Network Design: A Quadratic Assignment Approach

This paper introduces a novel Quantum Genetic Algorithm (QGA) designed to solve the Quadratic Assignment Problem (QAP), a structure widely applied in communication network optimization, where efficient solutions are critical. This approach utilizes quantum circuits as the objective function, enabling simultaneous evaluation of entire individuals at each generation of the Genetic Algorithm (GA) through quantum superposition and the SWAP test. A preprocessing step involving inverse stereographic projection is applied to ensure proper normalization of data. The method is validated in a case study, analyzing the impact of varying the number of elites and an additional algorithm parameter on solution quality and convergence speed. This work underscores the potential of quantum computing for addressing large-scale optimization challenges in communication networks.