Quantum Optimization for Finance: Algorithms, Hybrid Pipelines, and the State of Practical Advantage in the NISQ Era

Hangun B., ALTUN O., Eyecioglu O., KAYIŞLI K., Beken M.

2026 International Conference on Cybersecurity, Digital Forensics, and AI Applications, ICCSDFAI 2026, İstanbul, Türkiye, 25 - 27 Haziran 2026, ss.512-516, (Tam Metin Bildiri)

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/iccsdfai70505.2026.11647992
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.512-516
  • Anahtar Kelimeler: financial risk management, hybrid quantum-classical, portfolio optimization, QAOA, quantum annealing, quantum optimization, QUBO
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Many financial optimization problems are combinatorial in nature, including cardinality-constrained portfolio selection, risk-aware allocation under conditional value at risk (CVaR) and expected-shortfall objectives, and anti-money laundering (AML) graph analysis. These problems admit Quadratic Unconstrained Binary Optimization (QUBO) and Higher-Order Unconstrained Binary Optimization (HUBO) formulations and can be addressed by quantum optimization methods such as the Quantum Approximate Optimization Algorithm (QAOA), quantum annealing, and the Variational Quantum Eigensolver (VQE), often embedded in hybrid quantum-classical workflows. This review consolidates recent empirical evidence and finds that, at realistic problem scales, well-tuned classical solvers continue to outperform near-term quantum approaches. A representative benchmark reaches certified optimality on 250 instances with upto 1,000 assets using commercial mixed-integer programming solvers. Quantum methods remain competitive on small-to-medium instances, and risk-aware variants including CVaR-QAOA and HUBO-QAOA are a promising direction because their modified cost landscapes may be more amenable to variational optimization. Decomposition-based hybrid pipelines, combined with problem-tailored error mitigation and benchmarking against modern classical baselines, define a plausible trajectory toward practical near-term relevance.