How owlmt/qml works
This repository is a teaching collection of quantum machine-learning paper implementations. Its documented workflow is to run a paper notebook, obtain and preprocess its data, train a quantum model alongside classical baselines, and compare the results with the paper. The only implemented paper shown is Said’s DDoS-detection experiment. The notebook’s internal wiring is not available for inspection, so the graph reflects only the README’s documented stages and capabilities.
Main entry points: QSVM_DDoS_Said2023.ipynb Said DDoS notebook
Components 7
Data preparation
- CICDDoS2019 traffic
- Data preprocessing
papers/2023_Said_QSVM_DDoS/QSVM_DDoS_Said2023.ipynb
Model training
- ZZ-kernel QSVM
papers/2023_Said_QSVM_DDoS/QSVM_DDoS_Said2023.ipynb - Linear and RBF SVMs
papers/2023_Said_QSVM_DDoS/QSVM_DDoS_Said2023.ipynb
Evaluation and teaching
- Said DDoS notebook
papers/2023_Said_QSVM_DDoS/QSVM_DDoS_Said2023.ipynb - Results and paper comparison
papers/2023_Said_QSVM_DDoS/QSVM_DDoS_Said2023.ipynb
Other components
- Student or runner
Connections 8
- Student or runner → Said DDoS notebook: runs
- Said DDoS notebook → CICDDoS2019 traffic: downloads
- Said DDoS notebook → Data preprocessing: performs
- Data preprocessing → ZZ-kernel QSVM: provides inputs
- Data preprocessing → Linear and RBF SVMs: provides same inputs
- Said DDoS notebook → Results and paper comparison: documents results
- ZZ-kernel QSVM → Results and paper comparison: contributes results
- Linear and RBF SVMs → Results and paper comparison: contributes results