Quantum Portfolio Optimizer (QAOA) — 2D

Encode a five-asset portfolio-selection problem into a five-qubit register and run real QAOA on its exact 32-dimensional statevector, then watch a real classical coordinate-search optimizer tune the cost angle γ and mixer angle β of every layer, sweep by sweep, to raise the expectation value of the portfolio's risk-adjusted score. The bar chart shows the true Born-rule measurement probability of all 32 candidate portfolios under the best angles found so far; the convergence plot tracks that expectation value climbing toward the brute-force optimum. Tune the number of layers p and the risk-aversion λ, then run Optimize Step or Auto-Optimize to watch the same alternating cost/mixer circuit — and the same classical-optimizer outer loop — that IBM, Rigetti, and Google run on real superconducting qubits for combinatorial optimization.