Trash classification using quantum transfer learning.

Trash classification is an important activity which helps in identification of waste. In this paper, a Classical- Quantum Transfer learning model, namely TrashQNet is proposed to classify trash into two classes, Organic and Recyclable trash. Classical-Quantum Transfer learning is a combination of machine learning and quantum computing. The proposed TrashQNet model uses a pre-trained DenseNet169 network for the feature extraction process and a variational quantum circuit as a classifier. The performance of TrashQNet is compared with classical machine learning models K- Nearest Neighbour & Support Vector Machine, deep learning model - Convolutional Neural Network and transfer learning model. TrashQNet outperforms all these models, it achieves an accuracy of 94% on test dataset. [ABSTRACT FROM AUTHOR]