Quantum-Enhanced Similarity Measures for Polarimetric Materials Classification
We present a quantum--classical hybrid pipeline for polarimetric material classification that casts this as a point-matching problem. Voxel cubes, containing polarized light reflections, are used to train an encoder to produce 32-dimensional embeddings for the voxels of the cubes. At inference, the encoder head is discarded and the embeddings are encoded as probability amplitudes of quantum states. Next, a SWAP-test circuit estimates the fidelity between each of the 32D embeddings from the query cube and a dataset of anchor cubes. The aggregated fidelity serves as materials similarity scores, and the class of the anchor with highest aggregated fidelity is deemed as the class of the querie...
| Authors | Sara Shojaei, Seyed Mohamad Ali Tousi, Emma Bennett et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 June 2026 |
| Last updated at source | 5 June 2026 |
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