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.
Linear differential vision transformer: Learning visual contrasts via pairwise differentials
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VECA learns effective visual representations using core-periphery attention where patches interact exclusively via a resolution-invariant set of learned core embeddings, achieving linear O(N) complexity while maintaining competitive performance.
citing papers explorer
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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.
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Elastic Attention Cores for Scalable Vision Transformers
VECA learns effective visual representations using core-periphery attention where patches interact exclusively via a resolution-invariant set of learned core embeddings, achieving linear O(N) complexity while maintaining competitive performance.