# Quantum Neural Network

On hourly PM2.5 data from two roadside stations in Guildford, UK, a quantum neural network clearly beat classical MLP and LSTM baselines on raw, undecomposed series, with the depth-4 model lifting Pearson correlation from roughly 0.29–0.31 to 0.599. Adding five-level discrete wavelet decomposition, a 172-qubit depth-4 model reached the study's highest average correlation of 0.729 and lowest NMSE of 0.488 over the full six-hour horizon, though the classical MLP came close at that point. The results come from the Dynex neuromorphic quantum annealing emulation platform and have not been validated on physical quantum hardware.

On hourly PM2.5 data from two roadside stations in Guildford, UK, a quantum neural network clearly beat classical MLP and LSTM baselines on raw, undecomposed series, with the depth-4 model lifting Pearson correlation from roughly 0.29–0.31 to 0.599. Adding five-level discrete wavelet decomposition, a 172-qubit depth-4 model reached the study's highest average correlation of 0.729 and lowest NMSE of 0.488 over the full six-hour horizon, though the classical MLP came close at that point. The results come from the Dynex neuromorphic quantum annealing emulation platform and have not been validated on physical quantum hardware.