# The Quantum LLM: Modeling Semantic Spaces with Quantum Principles

arXiv: https://arxiv.org/abs/2504.13202 · HTML: https://mlsanity.com/paper/2504.13202/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 13 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Why does deep and cheap learning work so well?](https://mlsanity.com/paper/1608.08225/) (2016)
- [Semantic Wave Functions: Exploring Meaning in Large Language Models Through Quantum Formalism](https://mlsanity.com/paper/2503.10664/) (2025)

## Built on by (most cited first, 1 of 1)

- [Discrete Semantic States and Hamiltonian Dynamics in LLM Embedding Spaces](https://mlsanity.com/paper/2601.11572/) (2025)

## Abstract (arXiv)

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.