Mu-DisCoCat: A Variational Pipeline for Compositional Generalization on Quantum Processors

Key points

• Achieving compositional concept generalization (CoCoGen), the ability to understand novel situations by recombining learned primitives, remains a fundamental challenge in artificial intelligence.1
• Compositional semantic models such as Compositional Distributional Semantics (DisCoCat) offer solutions by generalising vectors to tensors, but suffer from scaling bottlenecks when learning the tensors.1
• Mapping DisCoCat onto Variational Quantum Circuits (VQCs) resolves this limitation for text, yet the methodology has not been expanded to multimodal situations such as the ones involved in CoCoGen.1
• Our work establishes a framework for executing CoCoGen on VQCs, demonstrating a viable use case for near-term quantum hardware.1

Sources (1)

• [1]Mu-DisCoCat: A Variational Pipeline for Compositional Generalization on Quantum Processors
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 10:45 AM

  This paper introduces Mu-DisCoCat: a multimodal variational quantum learning framework for DisCoCat that achieves CoCoGen.

  Achieving compositional concept generalization (CoCoGen), the ability to understand novel situations by recombining learned primitives, remains a fundamental challenge in artificial intelligence.

Extractive summary: sentences quoted from the sources.