Comparative study of the ansätze in quantum language models
Year: 2025
Type: preprint
Abstract: Quantum language models are the alternative to classical language models, which borrow concepts and methods from quantum machine learning and computational linguistics. While several quantum natural language processing (QNLP) methods and frameworks exist for text classification and generation, there is a lack of systematic study to compare the performance across various ansätze, in terms of their hyperparameters and classical and quantum methods to implement them. Here, we evaluate the performance of quantum natural language processing models based on these ansätze at different levels in text classification tasks. We perform a comparative study and optimize the QNLP models by fine-tuning several critical hyperparameters. Our results demonstrate how the balance between simplification and expressivity affects model performance. This study provides extensive data to improve our understanding of QNLP models and opens the possibility of developing better QNLP algorithms.
Source: arXiv (Cornell University)
Authors: Del Castillo, Jordi, Zhao, Dan, Pei, Zongrui
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6966745936
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