Robust Hybrid Classical-Quantum Transfer Learning Model for Text Classification Using GPT-Neo 125M with LoRA & SMOTE Enhancement

TL;DR

This paper presents a hybrid classical-quantum text classification model combining GPT-Neo, LoRA, and SMOTE, demonstrating improved accuracy and convergence using quantum computing backends, highlighting the potential of quantum-enhanced NLP.

Contribution

The paper introduces a novel hybrid classical-quantum framework for text classification that integrates GPT-Neo with LoRA and SMOTE, utilizing quantum computing backends for improved performance.

Findings

Hybrid model outperforms baseline GPT-Neo in accuracy.

LoRA and SMOTE enhance model convergence and generalization.

Quantum backends demonstrate viability for NLP tasks.

Abstract

This research introduces a hybrid classical-quantum framework for text classification, integrating GPT-Neo 125M with Low-Rank Adaptation (LoRA) and Synthetic Minority Over-sampling Technique (SMOTE) using quantum computing backends. While the GPT-Neo 125M baseline remains the best-performing model, the implementation of LoRA and SMOTE enhances the hybrid model, resulting in improved accuracy, faster convergence, and better generalization. Experiments on IBM's 127-qubit quantum backend and Pennylane's 32-qubit simulation demonstrate the viability of combining classical neural networks with quantum circuits. This framework underscores the potential of hybrid architectures for advancing natural language processing applications.