Effective dimension d_eff of the noise-shaped quantum feature kernel governs generalization in quantum kernel vision models, with entanglement and noise acting as regularization in overfitting regimes.
HQViT: Hybrid quantum vision transformer for image classification
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
QPSAN implements self-attention via PQCs with 5 parameters, establishes a theoretical framework for its scoring properties, and reports outperformance over ViT on four vision datasets that grows with data complexity.
Magnitude-only encoding reaches 99.57% accuracy on 3-class and 71.19% on 8-class SAR tasks in hybrid models, beating phase-inclusive alternatives, while phase boosts pure quantum models by up to 21.65 points.
citing papers explorer
-
Effective Dimension Governs Generalization in Quantum Kernel Vision Models
Effective dimension d_eff of the noise-shaped quantum feature kernel governs generalization in quantum kernel vision models, with entanglement and noise acting as regularization in overfitting regimes.
-
Quantum Parameterized Self-Attention Network for Image Classification
QPSAN implements self-attention via PQCs with 5 parameters, establishes a theoretical framework for its scoring properties, and reports outperformance over ViT on four vision datasets that grows with data complexity.
-
Magnitude Is All You Need? Rethinking Phase in Quantum Encoding of Complex SAR Data
Magnitude-only encoding reaches 99.57% accuracy on 3-class and 71.19% on 8-class SAR tasks in hybrid models, beating phase-inclusive alternatives, while phase boosts pure quantum models by up to 21.65 points.