The Quantum LLM: Modeling Semantic Spaces with Quantum Principles
This work addresses semantic modeling in AI, but it is incremental as it builds on prior quantum-inspired concepts without presenting new empirical results.
The paper tackles the problem of modeling semantic representation in Large Language Models by proposing a quantum-inspired framework, clarifying its core principles to justify its validity and discussing potential applications in quantum computing for more powerful LLMs.
In the previous article, we presented a quantum-inspired framework for modeling semantic representation and processing in Large Language Models (LLMs), drawing upon mathematical tools and conceptual analogies from quantum mechanics to offer a new perspective on these complex systems. In this paper, we clarify the core assumptions of this model, providing a detailed exposition of six key principles that govern semantic representation, interaction, and dynamics within LLMs. The goal is to justify that a quantum-inspired framework is a valid approach to studying semantic spaces. This framework offers valuable insights into their information processing and response generation, and we further discuss the potential of leveraging quantum computing to develop significantly more powerful and efficient LLMs based on these principles.