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All Journal Journal International of Lingua and Technology Journal International Inspire Education Technology (JIIET) Scientechno: Journal of Science and Technology International Journal of Educational Narratives International Journal of Language and Ubiquitous Learning International Journal of Research in Counseling Journal of Computer Science Advancements Journal of Noesantara Islamic Studies International Journal of Educatio Elementaria and Psychologia Research Psychologie, Orientation et Conseil Journal of Tecnologia Quantica Journal of Loomingulisus ja Innovatsioon Research of Scientia Naturalis Journal of Biomedical and Techno Nanomaterials Journal of Humanities Research Sustainability
Anis, Nina
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Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Physics Social Sciences Other

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Benchmarking Quantum Annealers vs. Classical Solvers for Complex Optimization Problems in Financial Modeling Nur, Muh.; Farah, Rina; Anis, Nina
Journal of Tecnologia Quantica Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i4.2601

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.
Co-Authors Alfiya, Luluk Ali, Makhrus Arifudin, Opan Asep Kusmawan Aziz, Saifiullah Chahine, Alaa Chahine, Alal Chai, Nong Daroini, Muchlis Dina Destari Farah, Rina Fariq, Aiman Ilhami, Mirza Indra Permana, Indra Irianti, Evi Joni Bungai, Joni Judijanto, Loso Krit, Pong Kusuma, Ruri Angelia Manurung, Edison Hatoguan Mei, Chen Muh Syafei Muh. Ilham Usman, Muh. Ilham Muh. Safar, Muh. Muhammad Hazmi Mujiburrohman Mujiburrohman Nizam, Zain Nur, Muh. Nurul Huda Rahman, Rashid Razak, Faisal Sestri, Elliya Shodiq, MD Zaman, Khalil