Quantum LLMs Using Quantum Computing to Analyze and Process Semantic Information

TL;DR

This paper introduces a quantum computing method for analyzing LLM embeddings, demonstrating the feasibility of using quantum hardware to estimate semantic similarity and revealing a connection between quantum mechanics and semantic representations.

Contribution

It establishes a novel approach to semantic analysis by mapping LLM embeddings to quantum circuits and experimentally calculating cosine similarity on a quantum computer.

Findings

Quantum approach can estimate semantic similarity between embeddings.

Experimental validation using a real quantum computer.

Links quantum mechanics principles with semantic representation.

Abstract

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.