A Hybrid Classical-Quantum Fine Tuned BERT for Text Classification

TL;DR

This paper introduces a hybrid classical-quantum BERT model for text classification, demonstrating competitive or superior performance to classical models and showcasing the potential of quantum computing in NLP tasks.

Contribution

It presents a novel hybrid classical-quantum approach to fine-tuning BERT, advancing quantum machine learning applications in text classification.

Findings

Hybrid model achieves competitive performance on benchmark datasets.

Quantum integration enhances adaptability across diverse datasets.

Demonstrates feasibility of quantum-classical models in NLP.

Abstract

Fine-tuning BERT for text classification can be computationally challenging and requires careful hyper-parameter tuning. Recent studies have highlighted the potential of quantum algorithms to outperform conventional methods in machine learning and text classification tasks. In this work, we propose a hybrid approach that integrates an n-qubit quantum circuit with a classical BERT model for text classification. We evaluate the performance of the fine-tuned classical-quantum BERT and demonstrate its feasibility as well as its potential in advancing this research area. Our experimental results show that the proposed hybrid model achieves performance that is competitive with, and in some cases better than, the classical baselines on standard benchmark datasets. Furthermore, our approach demonstrates the adaptability of classical-quantum models for fine-tuning pre-trained models across diverse datasets. Overall, the hybrid model highlights the promise of quantum computing in achieving improved performance for text classification tasks.