Hybrid Quantum Annealing with Innovative Trend Ratio Model for Portfolio Optimization

Hybrid quantum computing (QC) combines classical and quantum resources to tackle challenging optimization problems, leveraging the strengths of both within the current constraints of quantum computers, which are limited in size and power. Therefore, we explore the efficacy of a hybrid quantum annealing (QA) search algorithm in improving portfolio optimization. This study pioneers the application of the trend ratio (TR) to a quantum annealing computer, converting it into a constrained quadratic model with a flexible presentation. The TR serves as a promising indicator in portfolio evaluation, considering the great balance between expected returns and risks. Utilizing D- Wave's hybrid solver, we present a thorough analysis and discussion of the proposed model realized in the QA structure. The experimental results demonstrate that our model can discover solutions of comparable quality in a significantly shorter amount of time than an exhaustive search. When extending the search space to sizes challenging for exhaustive search, we conducted experiments comparing our approach with state-of-the-art quantum-inspired artificial intelligence (AI) algorithms. The results show that our method not only constructs higher-quality solutions but also requires the least computation time. The hybrid quantum-classical AI represents a forward-looking technology paradigm poised to revolutionize problem-solving methods.