research
Eleven Million Cells Keep the Same Clusters
A training-free quantum-classical encoding produced identical runs and processed whole-transcriptome atlases on one server.
Summary
A training-free quantum-classical encoding produced identical runs and processed whole-transcriptome atlases on one server.
KQE-2 maps cells to entanglement-free quantum states whose features can be evaluated exactly on classical hardware and checked on a quantum processor. The authors report that retraining scVI moved 14 percent of cells, while KQE-2 returned identical biological groupings across runs. Its encoding raised accuracy on a developing-cortex dataset from 0.61 to 0.79 and scaled to 11.4 million nuclei on one server, leading eight of ten complete atlases and staying within the best method's run-to-run range on the other two. These are preprint benchmark results, not evidence of quantum hardware speedup.
Why it matters
A training-free quantum-classical encoding produced identical runs and processed whole-transcriptome atlases on one server.
Limits and context
- These are preprint benchmark results, not evidence of quantum hardware speedup.
Key claims
A training-free quantum-classical encoding produced identical runs and processed whole-transcriptome atlases on one server.
Qualification: These are preprint benchmark results, not evidence of quantum hardware speedup.
Evidence: source-2026-11-10-006
Sources
- arXiv preprint 2611.11745, original v1arXiv · primary research
Corrections
No corrections have been recorded for this story.