Quantum vs Classical
Can quantum entanglement provide an advantage over classical CNNs in low-data medical imaging?
"Does adding a quantum processing layer improve deep learning performance for melanoma detection? I tested multiple Quanvolutional architectures against identical classical controls. After three failed experiments, Experiment 4 demonstrated a measurable Quantum Advantage."
4 Experiments. 3 Failures. 1 Breakthrough.
The Bottleneck
ConvNeXtBase → 8-Qubit Circuit
Information bottleneck. 1024 features crushed into 8 qubits.
Quanvolutional Layer
Quantum filter as feature extractor
Multiple iterations failed. Overfitting or identical performance.
Recall-Focused
Simplified circuit, 9,942 test images
High recall (79%) but low precision (42%). Output bottleneck.
High-Resolution
ResNet18 + Data Re-uploading + Projective Measurement
Quantum Advantage verified. +6.4% accuracy, +6.7% precision.
The Winning Architecture
ResNet18
Partially unfrozen backbone
High Resolution
196 feature patches (4x previous)
Data Re-uploading
Encoding data twice for non-linearity
Projective Measurement
256D output (vs 8 before)
Quantum vs Classical
Final results on the test set. Same architecture, same data—only the quantum layer differs.
Performance Comparison
Visual comparison of QCNN vs Classical CNN across key metrics. The quantum model shows consistent improvements in accuracy, precision, and F1-score.
Key Findings
Superior Generalization
The Anti-Overfitting Effect
The Classical model minimized training loss to 0.06 but validation loss spiked to 0.79. The QCNN maintained balance, acting as a powerful regularizer.
Escaping the Recall Trap
Learning to Discriminate
Classical models achieved high recall by classifying everything as positive. QCNN learned to discriminate—maintaining 85% recall while boosting precision.
Quantum Expressivity
High-Dimensional Feature Space
Data Re-uploading and Projective Measurements expanded features from 8 to 256 dimensions, enabling fine-grained pattern recognition.
Conclusion
This research proves that a Hybrid Quantum-Classical architecture, when properly designed with Data Re-uploading and High-Dimensional Measurements, can outperform an equivalent Classical architecture on tasks requiring generalization from limited data.
Related Project
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The high-performance ConvNeXt Ensemble achieving 98.59% recall for clinical melanoma detection.
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