Quantum Portfolio Optimization: Methods and Honest Limits
QuantufAI Labs ·
Portfolio optimization is the workload most often cited when quantum computing meets finance, and for a structural reason: it is a constrained combinatorial problem that grows hostile to classical solvers as asset counts, constraint interactions, and rebalancing frequency climb. It is also the workload where quantum marketing has historically been least honest. This post is about how we handle it — both the method and the claims.
How the workload is formulated
A portfolio problem arrives as assets, expected returns, a risk model, and constraints — position limits, sector exposures, turnover budgets. To run on quantum hardware it is reformulated as a quadratic optimization problem whose variables encode allocation decisions, which variational algorithms such as QAOA can then attack. The formulation step is classical, deterministic, and where most of the real engineering lives: a badly encoded constraint costs more solution quality than any hardware choice recovers.
How it is routed
Once formulated, the circuit goes through the same routing machinery as any other workload on the platform: transpile-time fit is computed per backend — circuit depth and two-qubit gate counts against each device's connectivity — and the router picks placement with cost and queue visibility up front, with every decision recorded against the calibration snapshot it was made on. Modeled scenarios on our industry pages walk through what that looks like for a mid-size portfolio, and they are labeled as exactly that: modeled.
The honest limits
Here is what we will not do: publish a customer name, a dollar improvement, or a Sharpe-ratio delta that we have not measured on real executions. An earlier draft of this post did — the figures were illustrative, the framing was not, and we removed them. Until real-hardware execution is generally available on the platform and produces measured results a customer signs off on, outcome numbers stay out of our copy.
What we can stand behind today is the method: the formulation is real, the transpile-time metrics are computed before any job is dispatched, and the cost model is visible before you spend anything. When there are measured outcomes worth publishing, they will come with their baselines, their backends, and their wall-clock times attached.
Describes workload formulation and routing methodology, not measured customer outcomes — job execution is currently gated.
— QuantufAI Labs