Medical Image Clustering & Segmentation
Tumor and seizure detection from imaging. Proven pilot, University of Valencia.
QG-CLUSTER
Bioinformatics & Medicine
Where the search space is biological, not just numerical.
Segmenting a medical image, aligning a molecular sequence, and screening drug candidates are all searches over vast combinatorial spaces. This is also where we have our clearest published result — a real clinical-imaging pilot, not a lab benchmark.
The problem
Segmentation, alignment and screening — all combinatorial search.
Clustering pixels or voxels into a tumor boundary, aligning sequences across a vast space of possible conformations, and screening candidate molecules against a target are all searches over intractably large spaces. Classical methods approximate; quantum-inspired and hybrid search explore more of the space at once.
What we optimize
Three solutions, three archetypes.
Molecular Conformation & Sequence Alignment
Optimization over vast biological sequence spaces.
QG-ROUTEQG-CLUSTER
Combinatorial Drug & Candidate Screening
Narrowing intractable candidate libraries to high-probability leads.
QG-SELECT
The engines behind it
Built on the same core engines.
Proof
A real, published pilot.
1Proven pilot: medical image clustering and segmentation, University of Valencia — tumor and seizure detection
Molecular conformation and drug-candidate screening are earlier-stage for this industry — no published number yet for those two. Same archetypes, same-caliber engines; a benchmark on your data is the next step.
Have an imaging, sequence, or screening problem to optimize?
Share a sample of your imaging, sequence, or candidate data; we'll benchmark against your current method and give you a clear read.
Request a pilot →