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 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
A circuit you build from that formulation goes through the same routing as any other workload on the platform, with cost and queue visibility before anything is dispatched. A portfolio table uploaded to the platform is solved classically, not on quantum hardware.
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 runs produce measured results a customer signs off on, outcome numbers stay out of our copy.
What we can stand behind today: 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.
— QuantufAI Labs