Quantum-Enhanced Similarity Measures for Polarimetric Materials Classification
For materials science and remote sensing, this work provides a proof-of-concept for NISQ-based material recognition, though it is incremental as it combines existing techniques (SWAP-test, embeddings) without major breakthroughs.
The paper introduces a quantum-classical hybrid pipeline for polarimetric material classification, achieving competitive accuracy with classical methods and demonstrating open-set discrimination potential on a dataset of 23 materials.
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.