Benchmarking Quantum Annealers vs. Classical Solvers for Complex Optimization Problems in Financial Modeling
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Classical Solvers, Financial Modeling, Quantum Annealing
Abstract
Quantum annealing has emerged as a promising computational paradigm for solving large-scale combinatorial optimization problems that are traditionally intractable for classical algorithms. The financial modeling sector, characterized by complex portfolio optimization, risk minimization, and option pricing problems, offers a fertile ground for benchmarking the performance of quantum versus classical solvers. This study aims to systematically evaluate the computational efficiency, scalability, and accuracy of quantum annealers specifically the D-Wave Advantage system against leading classical optimization algorithms, including simulated annealing and branch-and-bound methods. A comparative experimental framework was developed to test both solver types on real-world financial datasets encompassing portfolio selection and risk-parity optimization tasks. Quantitative performance metrics such as solution quality, convergence time, and energy landscape exploration were assessed. Results revealed that quantum annealers achieved near-optimal solutions significantly faster for high-dimensional problem instances with non-convex cost functions, whereas classical solvers maintained superior consistency for smaller, well-conditioned models. The findings suggest a complementary paradigm where quantum annealing can accelerate subproblems within hybrid financial optimization pipelines. The study concludes that quantum computing, while not yet universally superior, represents a viable accelerator for specific financial optimization classes under current hardware constraints.
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