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.
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.
We present a quantum computing approach to analyzing Large Language Model (LLM) embeddings, leveraging complexvalued representations and modeling semantic relationships using quantum mechanical principles. By establishing a direct mapping between LLM semantic spaces and quantum circuits, we demonstrate the feasibility of estimating semantic similarity using quantum hardware. One of the key results is the experimental calculation of cosine similarity between Google Sentence Transformer embeddings using a real quantum computer, providing a tangible demonstration of a quantum approach to semantic analysis. This work reveals a connection between LLMs and quantum mechanics, suggesting that these principles can offer new perspectives on semantic representation and processing, and paving the way for future development of quantum algorithms for natural language processing.
We present a quantum computing approach to analyzing Large Language Model (LLM) embeddings, leveraging complex-valued representations and modeling semantic relationships using quantum mechanical principles. By establishing a direct mapping between LLM semantic spaces and quantum circuits, we demonstrate the feasibility of estimating semantic similarity using quantum hardware. One of the key results is the experimental calculation of cosine similarity between Google Sentence Transformer embeddings using a real quantum computer, providing a tangible demonstration of a quantum approach to semantic analysis. This work reveals a connection between LLMs and quantum mechanics, suggesting that these principles can offer new perspectives on semantic representation and processing, and paving the way for future development of quantum algorithms for natural language processing.
Large Language Models (LLMs) encode semantic relationships in high-dimensional vector embeddings. This paper explores the analogy between LLM embedding spaces and quantum mechanics, positing that LLMs operate within a quantized semantic space where words and phrases behave as quantum states. To capture nuanced semantic interference effects, we extend the standard realvalued embedding space to the complex domain, drawing parallels to the double-slit experiment. We introduce a ”semantic wave function” to formalize this quantum-derived representation and utilize potential landscapes, such as the double-well potential, to model semantic ambiguity. Furthermore, we propose a complex-valued similarity measure that incorporates both magnitude and phase information, enabling a more sensitive comparison of semantic representations. We develop a path integral formalism, based on a nonlinear Schro¨dinger equation with a gauge field and Mexican hat potential, to model the dynamic evolution of LLM behavior. This interdisciplinary approach offers a new theoretical framework for understanding and potentially manipulating LLMs, with the goal of advancing both artificial and natural language understanding
Large Language Models (LLMs) excel in natural language tasks, but their high-dimensional embedding spaces pose significant interpretability challenges. Current approaches often linearize these spaces, overlooking the complex dynamics inherent in Transformer architectures. This article proposes a quantum framework to analyze LLM representations, leveraging quantum mechanical tools to explore semantic relationships and contextual influences. We introduce a layered hierarchy of semantic spaces and demonstrate that a classical LLM embedding system has an exact quantum mechanical analogue. Using this analogue, we model phenomena such as the modulation of Semantic Noise, the emergence of hallucinations via quantum tunneling, and the formation of stable semantic representations as soliton solutions. Furthermore, we present a simple quantum circuit design, demonstrating the possibility of using quantum computers to probe, analyze and go beyond real-valued LLM embedding spaces, potentially revealing structural information and relationships not readily accessible through classical techniques. This perspective enhances our understanding of LLM representations, leading to improved methods for analyzing and controlling LLM behavior, and supporting research into more efficient, reliable, and trustworthy AI systems.
This article explores the pioneering application of principles from quantum mechanics and statistical mechanics to the field of natural language processing (NLP). By drawing analogies between physical phenomena such as quantum entanglement, phase transitions, and statistical ensembles, and linguistic concepts like semantic relationships, language use dynamics, and lexical diversity, we offer a novel perspective on language analysis and processing. Quantum linguistic models, leveraging the intricacies of entanglement and quantum probability, provide a framework for understanding complex semantic networks and enhancing computational efficiency through quantum computing. Meanwhile, statistical mechanics inspires models for capturing lexical diversity and understanding the evolution of language patterns, akin to phase transitions in physical systems. This interdisciplinary approach not only deepens our understanding of linguistic phenomena but also introduces advanced mathematical and computational techniques to improve NLP tasks.
The convergence of quantum computing and large language models (LLMs) represents a major milestone in computational intelligence, introducing novel paradigms for information processing, complexity modeling, and scientific advancement. This survey provides a comprehensive overview of the emerging interdisciplinary field that integrates quantum mechanics with natural language processing. We begin by presenting the foundational principles of quantum computing and LLMs, then examine their integration through automated quantum code generation, hybrid transformer architectures, and quantum natural language processing (QNLP) frameworks. State-of-the-art models, such as quantum-augmented attention mechanisms and circuit embedding strategies, are evaluated across domain-specific datasets and benchmarks. We also investigate the role of LLMs as quantum research assistants, educational tools, and simulation agents, particularly in resource-constrained environments. Furthermore, the survey explores challenges related to scalability, interpretability, and quantum data encoding, and outlines future research directions-especially the vision for fully quantum-native LLMs. Our goal is to guide researchers through the current landscape and to encourage continued exploration at the intersection of quantum technologies and advanced language models.