Rapid identification of microplastic particles in water using geometric polarization characterization and quantum neural networks

The widespread pollution of water environments by microplastics (MPs) is a critical global issue. The identification of MPs remains challenging, primarily due to their limited texture and color information. To address this issue, this Letter proposes a learning-based approach that incorporates what we believe to be a novel geometric polarization characterization of MPs with quantum neural networks (QNNs). Our method comprises three stages. First, we establish a novel Polarization Angle-Weighted Geometric (PAWG) feature derived from the geometric relationship of polarization information. Second, a novel polarization-driven quantum convolutional network (PQCNet) is designed based on pure variational quantum circuits. Finally, the PAWG features are fed into the PQCNet to train an effective variational quantum classifier for intelligent identification of MPs. Experimental results show that the proposed method yields an average accuracy of approximately 95% across three MP types (i.e., PP, LDPE, and HDPE) in water. Further, the model with only 0.0023 M parameters enables rapid inference with an average prediction time of 11.3 ms per sample. Such a success rate and computational efficiency indicate that our method can be used to rapidly identify MPs.