mlsanityData to 14 Oct 2026

Quantum-Enhanced Similarity Measures for Polarimetric Materials Classification

arXiv 2606.07766 ↗First posted 5 Jun 2026cs.CV cs.AIcited by 0

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Abstract (arXiv)

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 queried material. We evaluate our approach on a dataset of 23 materials (≈800 samples each) derived from their Mueller matrices. The point-matching approaches from the proposed quantum SWAP-test and a classical classifier using Optimal Transport are compared. Our results demonstrate the competitive classification accuracy alongside open-set discrimination potential, establishing it as a viable path toward NISQ-based material recognition.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 31 references that are in the map. First posted on arXiv 5 Jun 2026.

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References · 16

  1. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  2. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  4. 2020Point Transformercited by 1,470
  5. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,287
  6. 2018Computational Optimal Transportcited by 780
  7. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 776
  8. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 653
  9. 2018Do Deep Generative Models Know What They Don't Know?cited by 443
  10. 2023OctFormer: Octree-based Transformers for 3D Point Cloudscited by 109
  11. 2022Dual Octree Graph Networks for Learning Adaptive Volumetric Shape Representationscited by 71
  12. 2021Deep Polarization Imaging for 3D shape and SVBRDF Acquisitioncited by 56
4 more
  1. 2025OctGPT: Octree-based Multiscale Autoregressive Models for 3D Shape Generationcited by 30
  2. 2021Q-Match: Iterative Shape Matching via Quantum Annealingcited by 27
  3. 2019A Quantum Computational Approach to Correspondence Problems on Point Setscited by 23
  4. 2023CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapescited by 11

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