Quantum-Enhanced Similarity Measures for Polarimetric Materials Classification

2026-06-05

AI Summary

This work addresses the challenges of similarity measurement and open-set recognition in polarimetric material classification by formulating the task as a point-matching problem. The authors propose a classical-quantum hybrid framework that maps Mueller matrices to 32-dimensional voxel embeddings via an encoder, representing them as probability amplitudes of quantum states. For the first time, a quantum SWAP-test circuit is employed to compute the fidelity between query samples and anchor points, which serves as the similarity score for open-set classification. Evaluated on a dataset comprising 23 material classes, the method achieves competitive classification accuracy and demonstrates strong open-set discrimination capability.

Abstract

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 ($\approx$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.

Problem

• Quantum Similarity
• Polarimetric Materials Classification
• SWAP-test
• Material Recognition
• NISQ

Innovation

• quantum SWAP-test
• polarimetric materials classification
• quantum-classical hybrid
• fidelity-based similarity
• NISQ