Feature & Subset Selection
Pruning intractable feature spaces to sharpen models.
QG-SELECT
AI & Foundational Models
The combinatorics behind building models.
Selecting features from an intractable space, searching architectures while scheduling scarce GPU capacity, and clustering large training sets are all combinatorial problems hiding inside a modeling pipeline that looks, on the surface, like pure machine learning.
The problem
Selection, scheduling and clustering, at model scale.
Feature spaces grow combinatorially with every column you add; architecture search multiplies candidate designs against a GPU cluster's finite capacity; clustering large, high-dimensional datasets gets exponentially harder as structure gets subtler. These are optimization problems wearing a machine learning costume.
What we optimize
Three solutions, three archetypes.
Architecture Search & Training-Queue Scheduling
Model structure optimization and GPU cluster scheduling.
QG-SELECTQG-SCHED
Quantum-Enhanced Clustering & Data Structuring
Superior grouping on large, complex datasets.
QG-CLUSTER
The engines behind it
Built on the same core engines.
Proof
No published number yet for this industry — and we're saying so.
QG-SCHED and QG-CLUSTER both carry real, published results elsewhere (telecom scheduling and medical imaging); QG-SELECT's portfolio research is mature but not yet independently benchmarked for feature selection. The honest version: mature archetypes, real research behind them, and when we run them against your pipeline, that benchmark is the number that counts.
Have a feature selection, architecture search, or clustering problem?
Share a sample of your feature set, search space, or dataset; we'll benchmark against your current pipeline and give you a clear read.
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