An Annealing-based Quantum Neural Network with Discrete Wavelet Transforms for Enhanced Air Pollution Nowcasting

This research project tackles the tough problem of modeling high‑entropy environmental data for urban air‑quality forecasts. Standard deep‑learning models often struggle with the fast‑changing, non‑linear behavior of PM2.5 concentrations. To get around this, we explore a scalable hybrid quantum‑machine‑learning framework that combines variational quantum circuits with quantum annealing on a neuromorphic quantum‑computing platform. First, we apply discrete wavelet transforms to break the environmental time series into multi‑resolution sub‑bands. This reduces input volatility and improves feature extraction. The framework can use up to 172 qubits and shows that wavelet preprocessing works well together with quantum neural networks. The approach provides a strong alternative to classical baselines such as Multilayer Perceptrons and

Long Short‑Term Memory networks, setting a benchmark for quantum usefulness in environmental engineering. The project gives step‑by‑step guidance on setting up the simulation, building the hybrid quantum‑classical pipeline, and measuring prediction accuracy with high‑density urban data. It points to an advanced research path for studying complex atmospheric dynamics.

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