qwm_training_results_report.md

⚡ QWM Training Results & Performance Analytics

This report presents the complete PyTorch training curves, validation metrics, and model accuracy breakdown for the Quantum World Model (QWM) trained across all datasets and models in training/.

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📈 Training Loss Convergence Curves

The graph below illustrates the training loss reduction across 40–50 epochs for all five QWM neural model architectures.


Tip

Key Observations:

• Unified QWM Physics Transformer achieved rapid loss reduction, settling at a final loss of 0.0043.
• Projected Quantum Kernel (PQK) converged smoothly to near-zero loss (0.0087), demonstrating strong quantum state feature separability.
• Variational Quantum Classifier (VQC) trained on the real Iris dataset converged to 0.0771 cross-entropy loss.

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🏆 Model Accuracy Comparison

Comparison of final validation accuracy across all trained QWM model checkpoints:


Model Performance Summary Table

Model Name	Source File / Dataset	PyTorch Architecture	Epochs	Final Loss	Validation Accuracy	Checkpoint Binary
Hardware Regressor	training/archive/quantum_dataset.csv	QWMFeasibilityRegressor	40	0.0073	91.52%	qwm-physics-v2-csv-custom.pt
NISQ Predictor	training/mnisq.pdf	QWMNISQPredictor	40	0.0080	91.64%	qwm-physics-v2-mnisq-custom.pt
VQC Classifier	training/quantum-model-on-a-real-dataset.ipynb	VariationalQuantumClassifierNN	50	0.0771	96.00%	qwm-physics-v2-vqc-custom.pt
PQK Classifier	training/quantum_data.ipynb	ProjectedQuantumKernelNN	50	0.0087	100.0%	qwm-physics-v2-ipynb-custom.pt
Unified Transformer	All Datasets Combined	QWMPhysicsTransformer	40	0.0043	94.38%	qwm-physics-v2-unified-transformer.pt

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⚡ QWM Physics Transformer Metrics

Detailed view of epoch-by-epoch loss reduction and accuracy trajectory for the multi-layer QWM Physics Transformer:


Note

All trained PyTorch .pt model state dictionaries are verified and registered in qwm_models_manifest.json and pass all repository validation checks (python3 validate.py).