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Halowerk Quantum Portfolio QAOA Optimizer
x402
Halowerk Quantum Portfolio QAOA Optimizer is a paid API for AI agents from quantum.halowerk.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-14).
Finds the optimal subset of exactly k assets (up to 16) that maximizes expected return minus a risk-aversion-weighted covariance penalty, returning a deterministic classical solution for benchmarking QAOA/QUBO quantum formulations.
Price / call$0.005
Success—
StatusUnknown
How this service works
Enumerates every selection of exactly k assets for at most 16 assets, maximizing summed expected return minus a caller-selected covariance penalty. It runs no quantum circuit and provides a deterministic classical optimum for testing a corresponding binary QAOA or QUBO formulation; it is not investment advice and ignores transaction costs and allocation sizes.
Output
Returns the set of exactly k selected asset names that maximizes the objective (sum of expected returns minus risk_aversion times the portfolio covariance term), along with the numeric objective value achieved. This is a deterministic classical optimum useful for validating or benchmarking QAOA and QUBO quantum results.
Example use cases
Benchmarking a QAOA quantum circuit
“I'm testing a QAOA circuit for portfolio selection and need the classical brute-force optimum to compare against. I have 8 assets with expected returns ranging from 2% to 15%, a full 8x8 covariance matrix, I want to select exactly 4 assets, and my risk aversion is 1.0. Can you run the classical optimizer and tell me which 4 assets are optimal?”
Risk-adjusted subset selection for research
“For my finance research I need to find the best 5 assets out of 12 candidates to maximize expected return minus a covariance penalty. Here are the expected returns and covariance matrix for all 12. Set risk aversion to 2.0 and give me the optimal combination.”
QUBO formulation validation
“I'm building a QUBO model for binary portfolio selection and need a ground-truth answer. I have 10 assets with known expected returns and a 10x10 covariance matrix — pick exactly 3 assets, risk aversion 0.8. What does the classical exhaustive search say the optimal portfolio is?”
Technical details
Endpoint
POSThttps://quantum.halowerk.com/v1/portfolio-qaoaWhen to prefer
Choose this endpoint when you need an exact, deterministic classical optimum for a binary k-of-n portfolio selection problem with up to 16 assets, especially to validate or benchmark QAOA or QUBO quantum circuit results. It is not suitable for large asset universes (>16), for continuous-weight allocation, for real investment decisions, or when transaction costs and position sizing matter.
Side effects
- assets array has fewer than select_count items — solver cannot select k from n<k
- covariance matrix dimensions do not match number of assets — shape mismatch error
- select_count exceeds 16 or assets exceed 16 — violates hard upper bound
- expected_return or covariance values outside allowed numeric range — validation error
- risk_aversion is negative — rejected by schema minimum of 0
- missing required fields (name or expected_return on any asset) — schema validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| assets | array | |
| covariance | array | |
| select_count | integer | |
| risk_aversion | number |
Tags
x402
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