https://github.com/sevdanurgenc/ml-vs-qml
It will be shown how to train a quantum machine learning model to overcome a classification problem.
https://github.com/sevdanurgenc/ml-vs-qml
classical-machine-learning quantum-machine-learning
Last synced: 30 days ago
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It will be shown how to train a quantum machine learning model to overcome a classification problem.
- Host: GitHub
- URL: https://github.com/sevdanurgenc/ml-vs-qml
- Owner: SevdanurGENC
- License: mit
- Created: 2023-07-17T20:42:30.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-07-17T20:51:40.000Z (almost 2 years ago)
- Last Synced: 2025-01-29T03:37:48.514Z (3 months ago)
- Topics: classical-machine-learning, quantum-machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 485 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ML-Vs-QML
It will be shown how to train a quantum machine learning model to overcome a classification problem.
As an example of a quantum model, we’ll train a variational quantum classifier (VQC). The VQC is the simplest classifier available in Qiskit Machine Learning and is a good starting point for newcomers to quantum machine learning who have a background in classical machine learning.
## Conclusion
```ts
print(f"Model | Test Score | Train Score")
print(f"SVC, 4 features | {train_score_c4:10.2f} | {test_score_c4:10.2f}")
print(f"VQC, 4 features, RealAmplitudes | {train_score_q4:10.2f} | {test_score_q4:10.2f}")
print(f"----------------------------------------------------------")
print(f"SVC, 2 features | {train_score_c2:10.2f} | {test_score_c2:10.2f}")
print(f"VQC, 2 features, RealAmplitudes | {train_score_q2_ra:10.2f} | {test_score_q2_ra:10.2f}")
print(f"VQC, 2 features, EfficientSU2 | {train_score_q2_eff:10.2f} | {test_score_q2_eff:10.2f}")
```
