arXiv:2607. 13466v1 Announce Type: new Abstract: Most multimodal learning methods improve how heterogeneous representations are aligned and fused, while post-fusion enhancement remains less explored.
Most multimodal learning methods improve how heterogeneous representations are aligned and fused, while post-fusion enhancement remains less explored. We propose Parallel Quantum Feature Augmentation (PQFA), a hybrid quantum-classical framework that applies multiple shallow variational quantum circuits to fused multimodal features.
arXiv:2608. 06846v1 Announce Type: cross Abstract: We test whether a parameterized quantum circuit (PQC) improves a hybrid quantum-classical model's performance on classical datasets, using an interface-matched classical map as the control while holding all other components fixed.
Large-scale Vision-Language Models have demonstrated impressive transfer learning capabilities across a wide range of tasks. For few-shot classification, we observe that VLMs exhibit a notable ability to filter candidate categories and thus achieve high Top-K accuracy.
arXiv:2607. 09113v1 Announce Type: cross Abstract: Data scarcity and class imbalance are persistent challenges in machine learning that degrade model generalization and introduce predictive bias.
The paper introduces Quantum-Inspired Nonlinear Adapters (QINA), compact modules that apply learnable trigonometric feature lifting followed by bounded nonlinear aggregation to pretrained vision models. QINA enables structured oscillatory basis functions with a norm-dependent Lipschitz bound, allowing spectral reshaping of representations without expanding the receptive field or significantly increasing parameters. Experiments on natural and medical imaging tasks show that QINA consistently outperforms identity baselines, fixed Fourier mappings, and parameter-matched generic adapters, demonstrating that geometry- and spectrum-aware adaptation is crucial for effective frozen-backbone transfer learning.
arXiv:2510. 03389v2 Announce Type: replace-cross Abstract: Current quantum computers require algorithms that use limited resources economically.
QiT is a Quantum‑Inspired Transformer designed for visual recognition tasks. It replaces quantum neural network concepts with scalable classical operations: angle‑inspired encoding of image tokens, self‑attention over periodic features approximating quantum fidelity kernels, and gated multiplicative emulation of variational circuit interactions. The model achieves competitive performance on image‑classification benchmarks, matching a classical Transformer while avoiding the high runtime costs of simulated quantum models.
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
arXiv:2608. 15601v1 Announce Type: new Abstract: Compositional Concept Generalization (CoCoGen), the ability to systematically recombine learned primitives in novel contexts, is a key challenge for multimodal learning.
arXiv:2509. 14026v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation functions.
arXiv:2606. 02785v1 Announce Type: new Abstract: Large machine learning models benefit substantially from multimodal inputs that provide a complementary view of the same example.
arXiv:2606. 26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations.