Enhanced Multi-Objective Quantum-Inspired Computing for Practical Portfolio Optimization

Quantum-inspired optimization (QIO) has gained attention for leveraging both quantum and classical advantages to enhance search efficiency in solving complex optimization problems on classical computers. While most QIO approaches have shown strong potential in addressing single-objective optimization, real-world problems often involve conflicting objectives, making them inherently multi-objective and challenging to solve. Unlike single-objective optimization, which seeks a single optimal solution, multi-objective optimization aims to obtain a set of Pareto-optimal solutions. This study extends QIO to multi-objective optimization (MoQIO) by incorporating quantum-inspired characteristics and provides an in-depth analysis of its performance. It utilizes quantum superposition and Q-gates to enhance convergence efficiency toward the Pareto front, while leveraging entanglement properties to expand solution diversity. Our results demonstrate that the proposed MoQIO effectively generates a diverse set of optimal portfolio solutions.