# Quantum Annealing

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.

This study built a gold-standard corpus of 1000 Portuguese outpatient clinical notes manually annotated by 5 trained researchers for 5 protected-entity categories (patient names, dates, identifiers, organizations, and geographic locations) and, on a held-out test set of 500 notes, compared two stand-alone LLMs (Llama-3.1-8B-instruct and Llama-3.3-70B-instruct) with two quantum-enhanced hybrid models (Dynex-QML with 8B and 70B base models, using QUBO formulations to transform the final attention layer into a global constraint satisfaction problem solved by neuromorphic quantum annealing); the quantum-enhanced Dynex-QML-70B achieved the highest macro-F1 of 0.855 (95% CI 0.823-0.880), above stand-alone Llama-3.3-70B (0.726), Dynex-QML-8B (0.733), and Llama-3.1-8B (0.

This study built a gold-standard corpus of 1000 Portuguese outpatient clinical notes manually annotated by 5 trained researchers for 5 protected-entity categories (patient names, dates, identifiers, organizations, and geographic locations) and, on a held-out test set of 500 notes, compared two stand-alone LLMs (Llama-3.1-8B-instruct and Llama-3.3-70B-instruct) with two quantum-enhanced hybrid models (Dynex-QML with 8B and 70B base models, using QUBO formulations to transform the final attention layer into a global constraint satisfaction problem solved by neuromorphic quantum annealing); the quantum-enhanced Dynex-QML-70B achieved the highest macro-F1 of 0.855 (95% CI 0.823-0.880), above stand-alone Llama-3.3-70B (0.726), Dynex-QML-8B (0.733), and Llama-3.1-8B (0.