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chirindaopensource / constrained_portfolio_optimization_via_quantum_approximate_optimization_algorithm

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End-to-End Python implementation of Mancilla et al.'s (2026) methodology for solving the direct indexing portfolio selection problem as quantum combinatorial optimization. Enforces cardinality constraints via subspace confinement. Benchmarks PennyLane quantum circuits against D-Wave simulated annealing & HRP baselines with walk-forward backtesting.

python jupyter-notebook quantum-computing scipy portfolio-optimization quantitative-finance simulated-annealing operations-research quantum-algorithms mean-variance-optimization combinatorial-optimization computational-finance financial-econometrics qaoa research-implementation pennylane direct-indexing hamiltonian-simulation variational-quantum-algorithms d-wave-ocean
  • Updated Mar 4, 2026
  • Jupyter Notebook

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