SQFE: A Scalable Quantum Feature Extraction Model for Deep Neural Networks
Bibliographic record
Abstract
Convolutional neural networks (CNNs) have demonstrated strong performance in image processing tasks. However, as the complexity of the image scene increases, their ability to efficiently extract meaningful features diminishes. Quantum computing is increasingly recognized for its potential to address this limitation. We propose Scalable Quantum Feature Extraction (SQFE), a novel quantum feature extraction model designed as a variational quantum circuit (VQC) that mimics convolutional behavior while leveraging quantum principles. Unlike prior quantum feature extraction models that discard spatial features, use fixed quantum filters, or prevent joint optimization, SQFE supports full end-to-end training through backpropagation and is compatible with deep neural networks. SQFE is integrated into a ResNet-18 network to form the hybrid model QuRes. We evaluate multiple QuRes variants on a real-world dataset and demonstrate that QuRes models achieved test accuracies between 86.15% and 87.82%, outperforming the classical ResNet-18 with 84.84% accuracy. In terms of model complexity, QuRes reduced total parameter counts by 65%. These results highlight the potential of hybrid quantum-classical models to improve efficiency, scalability, and learning capacity in machine learning applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".