A Quantum Machine Learning Approach to Spatiotemporal Emission Modelling
Bibliographic record
Abstract
Despite the growing impact of emissions on our health and the environment, there remains an unmet need for emission concentration prediction and forecasting. The accumulating monitoring station and satellite data make the problem well-suited for quantum machine learning. This work takes a quantum machine learning approach to the spatiotemporal prediction of emission concentration. A quantum quanvolutional neural network model was developed and compared to a classical spatiotemporal ConvLSTM model using an evaluation framework of baseline models and metrics of per-pixel loss and intersection over union accuracy. The quantum quanvolutional neural network developed successfully generates one-hour-ahead emission concentration forecasts with increasingly lower loss (6.5% and 30.5% less) and higher accuracy (18.4% and 18.6% higher) compared to the input-independent and random baselines at the end of training. The quantum model was also comparable to the classical ConvLSTM model, with slightly lower loss (4%) but also slightly lower accuracy (3.7%). The study’s results suggest that the quantum machine learning approach has the potential to improve emission concentration modeling and could become a powerful tool for accurately predicting air pollution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".