Quantum

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

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Briefings

A study using classically simulated quantum layers evaluated their impact on Vision-Language Models (VLMs) by comparing classical and hybrid architectures. Inserting quantum layers after the visual projector improved accuracy by 80.7% on the Visual Spatial Reasoning dataset, while replacing the final decoder feed-forward sublayer reduced trainable parameters by 13.07 million. These benefits are highly location-dependent, with earlier replacements lowering accuracy and fewer parameters not reliably shortening training.

Key points

• Inserting a quantum layer after the visual projector in LLaVA-OneVision improved accuracy by 80.7% on the Visual Spatial Reasoning dataset compared to classical fine-tuning.
• Replacing the final decoder feed-forward sublayer reduced trainable parameters by 13.07 million while retaining 79.22% accuracy.

Context

This research investigates whether quantum components can enhance large multimodal models beyond theoretical concepts, using simulated quantum circuits within a VLM framework.

Limitations

Findings are based on classically simulated quantum layers; generalization to real quantum hardware remains unverified. Results depend on specific insertion and replacement locations, with earlier replacements showing lower accuracy and fewer parameters not reliably reducing training cost.

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