A Hybrid Classical-Quantum Fine Tuned BERT for Text Classification
This work addresses computational efficiency and performance in text classification for ML practitioners, but it appears incremental as it builds on existing quantum and classical methods.
The authors tackled the computational challenges of fine-tuning BERT for text classification by proposing a hybrid classical-quantum BERT model, which achieved competitive or better performance than classical baselines on standard benchmarks.
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.