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

Key points

• Quantum components are expected to extend the capabilities of existing AI systems, yet it remains unclear how such components will help within large multimodal models.1
• In this study, we try to answer that question for vision-language models (VLMs) using classically simulated quantum layers.1
• With LLaVA-OneVision as the backbone, we evaluate 4- and 8-qubit basic-ring and strongly entangling circuits at four insertion and four replacement locations.1
• To assess potential security benefits, we evaluated the robustness of quantum-classical hybrid models under six attack settings.1

Sources (1)

• [1]Can Quantum Layers offer Performance and Security Advantages in Vision-Language Models?
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 10:28 PM

  As quantum computing moves from the noisy intermediate-scale quantum (NISQ) era toward early fault-tolerant quantum computing (EFTQC), quantum-enhanced AI is shifting from a conceptual idea to experimental research.

  Quantum components are expected to extend the capabilities of existing AI systems, yet it remains unclear how such components will help within large multimodal models.

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