qaoa_portfolio_optimization.py — QAOA Benchmark for Cardinality-Constrained Portfolio Selection
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901642172956672 |
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| author | B, Britt |
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| contents | <pre><code>qaoa_portfolio_optimization.py v1.0 — QAOA Benchmark for Cardinality-Constrained Portfolio Selection Features • Zero extra setup core — single file (numpy/scipy + matplotlib for plots) • Modern Markowitz optimization with realistic cardinality constraint • Exact integer solution via mixed-integer programming (PuLP) • QAOA-inspired warm-start heuristic (greedy + local search) • Efficient frontier visualization with optimal portfolio highlighted • Professional risk-return metrics including Sharpe ratio • Realistic 12-asset universe with correlated returns Dependencies • Requires numpy>=1.21 • Requires scipy>=1.8 • Requires matplotlib>=3.5 — only for --plot • PuLP recommended for --method exact (falls back to heuristic if missing) Intended for portfolio managers, quantitative researchers, and quantum finance scientists studying constrained optimization problems suitable for near-term QAOA advantage. Real usage: python qaoa_portfolio_optimization.py python qaoa_portfolio_optimization.py --assets 8 --method heuristic --plot python qaoa_portfolio_optimization.py --assets 5 --plot Made by Britt (2025) — MIT License</code></pre> |
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| id | zenodo_https___doi_org_10_5281_zenodo_18079637 |
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| publishDate | 2025 |
| publisher | Zenodo |
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| spellingShingle | qaoa_portfolio_optimization.py — QAOA Benchmark for Cardinality-Constrained Portfolio Selection B, Britt QAOA portfolio optimization cardinality constraint markowitz model quantum approximate optimization QUBO finance constrained portfolio selection efficient frontier sharpe ratio quantum finance benchmark mixed integer programming quantum advantage quantum finance python cli tool single-file script <pre><code>qaoa_portfolio_optimization.py v1.0 — QAOA Benchmark for Cardinality-Constrained Portfolio Selection Features • Zero extra setup core — single file (numpy/scipy + matplotlib for plots) • Modern Markowitz optimization with realistic cardinality constraint • Exact integer solution via mixed-integer programming (PuLP) • QAOA-inspired warm-start heuristic (greedy + local search) • Efficient frontier visualization with optimal portfolio highlighted • Professional risk-return metrics including Sharpe ratio • Realistic 12-asset universe with correlated returns Dependencies • Requires numpy>=1.21 • Requires scipy>=1.8 • Requires matplotlib>=3.5 — only for --plot • PuLP recommended for --method exact (falls back to heuristic if missing) Intended for portfolio managers, quantitative researchers, and quantum finance scientists studying constrained optimization problems suitable for near-term QAOA advantage. Real usage: python qaoa_portfolio_optimization.py python qaoa_portfolio_optimization.py --assets 8 --method heuristic --plot python qaoa_portfolio_optimization.py --assets 5 --plot Made by Britt (2025) — MIT License</code></pre> |
| title | qaoa_portfolio_optimization.py — QAOA Benchmark for Cardinality-Constrained Portfolio Selection |
| topic | QAOA portfolio optimization cardinality constraint markowitz model quantum approximate optimization QUBO finance constrained portfolio selection efficient frontier sharpe ratio quantum finance benchmark mixed integer programming quantum advantage quantum finance python cli tool single-file script |
| url | https://doi.org/10.5281/zenodo.18079637 |