Quantum Optimization Algorithms for Portfolio Management and Risk Minimization in Banking

Abstract

The rapid development of quantum computing presents transformative opportunities for financial services, particularly in portfolio management and risk minimization. Traditional optimization methods, including mean-variance analysis and heuristic algorithms, face scalability and computational limitations when applied to large, complex financial datasets. Quantum optimization algorithms, leveraging principles such as superposition, entanglement, and quantum annealing, provide a pathway to efficiently solve combinatorial and high-dimensional optimization problems inherent in banking operations. This paper explores the theoretical foundations of quantum optimization, its application to portfolio allocation, risk reduction, and derivative pricing, and examines implementation challenges, hybrid classical-quantum architectures, and future directions. A comprehensive discussion is provided on algorithmic design, including Quantum Approximate Optimization Algorithms (QAOA), Variational Quantum Eigensolvers (VQE), and quantum-inspired methods, emphasizing their integration with machine learning frameworks for predictive analytics. The analysis highlights regulatory, computational, and ethical considerations, situating quantum optimization within the broader landscape of financial technology. Ultimately, this study demonstrates how quantum-enabled strategies can enhance decision-making in banking while managing uncertainty and exposure in a rapidly evolving financial environment.