Quantum-inspired deep learning model for organic municipal solid waste classification toward a circular bioeconomy.

Organic waste constitutes a major portion of total municipal solid waste and plays a critical role in sustainable waste management. Proper segregation of organic waste at the source is necessary for improving the waste processing efficiency and facilitating effective resource recovery. However, many organic waste categories share overlapping visual features, which makes it difficult for conventional deep learning models to clearly distinguish between organic waste subclasses. To overcome this limitation, the proposed study introduces Quantum BioNet 2.0, a hybrid quantum-classical framework designed for structured organic waste classification. The proposed model integrates ResNet50 based feature extraction with parallel classical dense layers and an eight-qubit variational quantum circuit, followed by feature fusion for decision making. The framework was trained and evaluated on a curated dataset of 9000 waste images collected from multiple public sources and organized into hierarchical binary and fine-grained organic categories. The experimental evaluation follows a two stage hierarchical structure. Stage 1 performs binary classification of organic and inorganic waste, achieving 99.44% accuracy with consistent improvement over InceptionResNetV2, InceptionV3, MobileNetV2, and DenseNet121 under the same experimental setup, thereby ensuring reliable routing to the subsequent processing. Only samples identified as organic are then passed to stage 2 for fine-grained classification. Under this structured pipeline, Stage 2 achieved 98.35% accuracy with an AUC of 99.96%, outperforming the classical baseline models such as MobileNetV2 under identical training conditions.These findings demonstrate that structured hybrid quantum-classical feature fusion provides measurable performance gains over state-of-the-art convolutional models in fine-grained organic waste classification. The proposed framework thus supports source level segregation and can assist automated waste sorting systems by improving subclass identification and material recovery. Future work involves validating the model on large-scale and multi-regional datasets and examining its feasibility for real-time deployment in operational waste management settings.