发表机构
University College London; University of Amsterdam（伦敦大学学院; 阿姆斯特丹大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出Mu-DisCoCat，一种多模态变分量子学习框架，通过先学习稳定对象表示再学习关系，在量子处理器上实现组合概念泛化，并在模拟和真实量子硬件上验证了其有效性。
AI 中文摘要
实现组合概念泛化（CoCoGen），即通过重组已学习的基元来理解新情境的能力，仍然是人工智能中的一个基本挑战。组合语义模型，如组合分布语义（DisCoCat），通过将向量推广为张量来提供解决方案，但在学习张量时面临扩展瓶颈。将DisCoCat映射到变分量子电路（VQCs）上解决了文本方面的这一限制，但该方法尚未扩展到多模态情境，例如CoCoGen所涉及的那些情境。本文介绍了Mu-DisCoCat：一个用于DisCoCat的多模态变分量子学习框架，实现了CoCoGen。该框架首先从单对象图像-文本对中学习稳定的对象表示，然后固定这些表示，并利用它们学习多对象情境中对象之间的关系。在经典模拟中，该模型使用Uhlmann态保真度来计算多模态电路表示之间的重叠，并取得了比所评估的CLIP基线更高的关系性OOD准确率。其部署通过破坏性SWAP测试在含噪量子模拟器上进行了评估，包括一系列IBM假后端、IQM FakeAphrodite以及IBM Marrakesh量子处理器。尽管存在真实世界的设备噪声，硬件执行的模型与模拟保真度保持了强正相关，能够可靠地区分未见过的相似和不相似对。我们的工作建立了一个在VQCs上执行CoCoGen的框架，展示了近期量子硬件的一个可行用例。
英文摘要
Achieving compositional concept generalization (CoCoGen), the ability to understand novel situations by recombining learned primitives, remains a fundamental challenge in artificial intelligence. 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. 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. This paper introduces Mu-DisCoCat: a multimodal variational quantum learning framework for DisCoCat that achieves CoCoGen. The framework first learns stable object representations from single-object image-text pairs, then fixes these and uses them to learn the relations between them in multi-object situations. In classical simulations, the model used Uhlmann state fidelity to compute the overlap between the multimodal circuit representations and achieved higher relational OOD accuracy than the evaluated CLIP baseline. Its deployment was evaluated using the destructive SWAP test across noisy quantum emulators, including a range of IBM fake backends, IQM FakeAphrodite, and the IBM Marrakesh quantum processor. Despite real-world device noise, the hardware-executed models maintained a strong positive correlation with simulated fidelities, reliably distinguishing unseen similar and dissimilar pairs. Our work establishes a framework for executing CoCoGen on VQCs, demonstrating a viable use case for near-term quantum hardware.
