Quantum-Driven Financial Decision-Making Models for Management Science and Strategic Planning

The increasing complexity of financial systems and strategic environments has intensified the need for advanced decision-making models capable of handling uncertainty, multidimensional risk, and dynamic optimization constraints. Classical management science tools—while robust—often face computational limitations when applied to high-dimensional portfolio optimization, capital allocation, and scenario-based strategic planning. This paper explores quantum-driven financial decision-making models for management science and strategic planning, emphasizing hybrid quantum–classical frameworks that integrate quantum optimization, probabilistic sampling, and advanced analytics into managerial decision systems. By examining applications in portfolio management, risk assessment, supply chain finance, and strategic resource allocation, the study evaluates the theoretical advantages and practical constraints of quantum-enhanced models in organizational contexts. The findings suggest that quantum-driven decision architectures may complement traditional financial models by expanding solution-space exploration and improving optimization efficiency, thereby enhancing strategic agility and long-term value creation.