Portfolio Optimization
Return maximization under risk and regulatory constraints.
QG-SELECT
Finance
Portfolios, fraud and capital — the same hard mathematics.
Constructing a portfolio, isolating fraud in a transaction graph, and allocating capital under exposure limits are all combinatorial selection and partitioning problems. Classical solvers approximate; we prove the difference on your own data first.
The problem
Selection and partitioning under real-world limits.
Portfolio construction is a knapsack-shaped selection problem under risk and regulatory constraints; fraud detection is pattern search across a transaction graph; capital allocation is selection and packing at once. All three get harder combinatorially as the candidate set grows — exactly where classical heuristics start approximating rather than solving.
What we optimize
Three solutions, three archetypes.
Fraud & Anomaly Detection
Combinatorial pattern search across transaction graphs.
QG-CLUSTER
Risk-Constrained Capital & Credit Allocation
Exposure optimization under portfolio limits.
QG-SELECTQG-ALLOC
The engines behind it
Built on the same core engines.
Proof
No published number yet for this industry — and we're saying so.
QuMatrix has working research code for both a quantum stock-portfolio application and quantum fraud detection, but neither has an independently benchmarked figure published for finance specifically yet. The honest version: mature archetypes, real research behind them, and when we run either engine against your data, that benchmark is the number that counts.
Have a portfolio, fraud, or allocation problem to optimize?
Share a sample of your data (or a synthetic equivalent); we'll benchmark against your current model and give you a clear read.
Request a pilot →