Methods › Computer Vision › Backbone Architectures › PoAPL
Polar Angle Prediction Layer
PoAPL
Introduced by Vinayak Sharma et al. in Quantum Polar Metric Learning: Efficient Classically Learned Quantum Embeddings
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
A trainable layer that encodes feature vectors onto 2 rotational coordinates R_y(θ),R_z(γ) for a unit sphere. Used to map data onto the Bloch Sphere surface for qubits
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Quantum Polar Metric Learning: Efficient Classically Learned Quantum Embeddings 4 Dec 2023 · 0 repositories · arXiv:2312.01655
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Metric Learning | 1 |
| Quantum Machine Learning | 1 |
| Triplet | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections