Reproducible QUBO-QAOA Benchmark for Cardinality-Constrained Mean-Variance Portfolio Selection
2026 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2026, Rome, İtalya, 6 - 09 Temmuz 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/icecet65726.2026.11632298
- Basıldığı Şehir: Rome
- Basıldığı Ülke: İtalya
- Anahtar Kelimeler: artificial intelligence, data science, optimization, portfolio optimization, quadratic unconstrained binary optimization (qubo), quantum approximate optimization algorithm (qaoa)
- Marmara Üniversitesi Adresli: Evet
Özet
Cardinality-constrained mean-variance portfolio selection is a practical decision-support problem in finance and business analytics and a compact benchmark for combinatorial optimization. A Quadratic Unconstrained Binary Optimization (QUBO) model is derived using an equal-weight utility proxy and an instance-adaptive quadratic penalty that enforces a fixed number of selected assets. A depth-one Quantum Approximate Optimization Algorithm (QAOA) solver is evaluated on a classical statevector simulator and compared against an exact feasibleenumeration baseline on small problem sizes. A fully reproducible Python implementation is described, including QUBO construction, penalty calibration, and a (γ, β) grid-search protocol that minimizes expected energy, followed by a top- M post-processing rule for returning a feasible portfolio. Across 10 synthetic instances per problem size, feasibility is achieved in all runs for N ∈{8,10,12} with K=3 under the default setting (α=10, M=256). An ablation quantifies that top-M post-processing is critical for maintaining feasibility under a standard mixer, while penalty scaling exhibits an explicit feasibility-quality trade-off at N=12. A simple classical greedy and random- K baseline is also reported to contextualize depth-one QAOA performance.