Quantum‐Inspired Genetic Algorithm for Influence Maximization on Social Networks

ABSTRACT The influence maximization (IM) problem focuses on identifying key nodes in social networks to maximize influence spread. IM is crucial in various applications such as viral marketing, information diffusion, advertisements, political campaigns, etc. Quantum‐inspired approaches enable the faster exploration of solutions. This work presents a quantum‐inspired genetic algorithm (QiGA) to address the IM problem. It maps the QiGA principles to solve the IM problem and proposes a novel strategy for encoding the influence diffusion rules using quantum circuits. This work evaluates the performance of the proposed algorithm against benchmark algorithms on synthetic and real‐world data sets. Experimental results demonstrate that the proposed algorithm outperforms existing algorithms on smaller networks and remains competitive in large‐scale networks. The execution time is acceptable, considering the quantum code is simulated on classical hardware. Thus, this research introduces quantum‐computing‐based QiGA algorithm to solve IM problem. Future research directions include implementing the algorithm on real quantum hardware and addressing IM using quantum computing‐based heuristics, such as node ranking, for further improvements.