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arXiv Machine Learning By Md Khalid Syfullah, Alvi Ataur Khalil

Can Quantum Layers offer Performance and Security Advantages in Vision-Language Models?

Read the original on arXiv Machine Learning →

The paper investigates the use of classically simulated quantum layers in vision‑language models, specifically adding or replacing components in the LLaVA‑OneVision backbone. Experiments with 4‑ and 8‑qubit circuits show that inserting a quantum layer after the visual projector can raise development accuracy by 2.28 percentage points, while replacing the final decoder feed‑forward sublayer reduces trainable parameters by 13.07 million with only a modest drop in accuracy. The study also evaluates robustness against six attack scenarios, mapping where quantum layers improve or degrade performance and security in multimodal models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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