Microplastics are a serious threat to human health and the environment, mainly because of their persistence and entry into the food chain through marine life. The weathering of these particles, caused by prolonged exposure to UV radiation and heat, accelerates chemical changes that can harm ecosystems and human health and make them hard to identify. Therefore, it is important to accurately identify and classify microplastics to assess their impact and creating efficient mitigation measures. This study includes quantum machine learning together with traditional deep learning methods, such as autoencoders, and statistical approaches like principal component analysis and linear discriminant analysis, to classify Raman spectra of different microplastic samples. The model proposed classifies microplastics into weathered and standard samples with an accuracy of 94%, distinguishing between distinct types of plastics, such as polyvinyl chloride, polyethylene terephthalate, polypropylene, polyethylene, and polyamide at 98.5% accuracy. In order to further improve this research forward, the model was run on live quantum computing hardware using Amazon Braket services. The end-to-end pipeline was run on both Braket's SV1 state vector simulator and the IonQ Aria quantum hardware, utilizing the compute resources of AWS's quantum environment. The model recorded a performance level of 97%–98% in the focused tasks, establishing the applicability of embedding quantum computing into practical applications. By improving the precision of microplastic identification, this study adds to the better understanding of their environmental impact and aids wider efforts toward reducing related ecological risks.
Kulkarni et al. (2026) studied this question.