Solver engine · Design partner · Partitioning & Clustering
QG-CLUSTER.
What are the natural groups? QG-CLUSTER partitions and segments large, complex datasets — from transaction graphs to medical images — to surface structure classical methods miss.
Overview
Partitioning & Clustering.
Medical imaging, Valencia
Tumor and seizure detection from imaging data, with the University of Valencia — a real, published pilot result.
Working with early adopters
QG-CLUSTER is co-developed with design partners across finance and life sciences before wider release.
Your data
We benchmark against your current clustering or segmentation approach before anything changes.
Canonical problems
What QG-CLUSTER solves.
Industries
Where it applies.
Methods
Quantum-enhanced clustering.
QG-CLUSTER formulates partitioning and clustering problems as max-cut and related QUBO/Ising instances, solved with quantum annealing and quantum-inspired search — well-suited to the graph structure behind segmentation, cohorting and anomaly detection.
Research behind this engine
Quantum image clustering and segmentation, and quantum fraud detection — developed at QuMatrix, including the medical-imaging work behind the Valencia pilot.
Proof of value
A proven pilot, with more on the way.
The Valencia result is real and published; it does not automatically transfer to finance or AI use cases. For those, the honest version is: same archetype, same-caliber techniques, benchmarked on your data before anything changes.
FAQ
Straight answers.
Is this actually quantum?
Yes — the Valencia pilot and our fraud-detection research both run on quantum annealing and quantum-inspired search. We're transparent about which solver handled which job.
What's the maturity?
Design partner. One domain (medical imaging) has a proven, published pilot; others (finance, AI) are being co-developed with early design partners.
Does the Valencia result apply to my use case?
Not automatically — it's evidence the archetype and methods work, not a guarantee for your specific data. We benchmark on your data before we make any claim about your case.
What data do you need to start?
The dataset you currently cluster or segment, your current method's output, and the constraints that matter to you. Anonymized or sampled data is fine for a first benchmark.
See QG-CLUSTER run against your data.
Thirty minutes to scope a pilot, a clear benchmark on your own data, and an honest read on whether it's worth going further.
Contact us →