Abstract:

Quantum computing possesses revolutionary capability in transforming the face of computationally complex operations, especially within financial engineering when optimization problems involve enormous and multidimensional ones. Its capacity for processing and handling enormous amounts of data in a single instance forms a paradigm shift in the development and implementation of financial models. Existing optimization techniques in financial engineering, including Monte Carlo simulations and linear programming, are generally computationally expensive, plagued by scalability, and ill-suited to explore high-dimensional solution spaces. These shortcomings result in low-quality solutions and excessive decision-making latency. To overcome these, we introduce the Quantum-Enhanced Adaptive Financial Optimization Framework (QEAFOF). This paradigm combines hybrid quantum-classical algorithms and quantum annealing with machine learning strategies to speed convergence, enhance the quality of the solution, and better handle high-dimensional financial information. QEAFOF is used in portfolio optimization, risk evaluation, and derivative pricing and facilitates real-time decision-making as well as enhanced financial prediction accuracy. Its adaptive nature allows for simplicity in adoption within current financial infrastructure, ensuring improved robustness and transparency. Experimental outcomes show that QEAFOF performs better compared to traditional models in the context of speed, solution quality, and efficiency. These outcomes prove the effectiveness of quantum-inspired methods in remapping the future of financial optimization.