We introduce Quantum-KIP, a method that compresses a training set into a small set of kernel inducing points with soft labels. It uses a quantum feature map to compute state-fidelity overlaps and relies only on forward evaluations, avoiding backpropagation through quantum circuits. We provide a compression-induced stability analysis showing that replacing one training example changes the learned set and predictions by $O(m/n)$ . We further provide a joint analysis of this sensitivity bound with intrinsic quantum noise, showing how finite-shot measurement noise and depolarizing noise give rise to privacy-relevant distinguishability bounds for quantum-kernel observations. A circuit-execution analysis shows substantially fewer quantum runs than gradient-based approaches. On MNIST and CIFAR-10 datasets with six-qubit feature maps, Quantum-KIP achieves accuracy close to full-data training, large speedups, reduced privacy leakage, and robustness under depolarizing and measurement noise.
Baobao Song, Shiva Raj Pokhrel, Athanasios V. Vasilakos et al.· IEEE Transactions on Informa...· 0 citations
Large Action Models (LAMs) extend the capabilities of AI systems beyond text generation toward perception, reasoning, and action, enabling applications across robotics, autonomous systems, smart manufacturing, healthcare, and the Internet of Things. This Special Issue of ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) brings together five contributions addressing key challenges in LAM research, including safety and robustness against jailbreak and adversarial attacks, semantic-perceptual integration for robotic manipulation, efficient deployment on edge devices, and natural-language-driven decision-making for networked systems. Together, these papers span the theoretical, implementation, and application dimensions of LAMs, offering both practical solutions and a foundation for future research toward LAM-based systems that are safe, efficient, and reliably grounded in action.
M. Gabbouj, Jin Li, Xin Lin et al.· ACM Transactions on Multimed...· 0 citations
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