When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing

Abstract: The rapid development of quantum computing has demonstrated many unique characteristics of quantum advantages, such as richer feature representation and more secured protection on model parameters. This work proposes a vertical federated learning architecture based on variational quantum circuits to demonstrate the competitive performance of a quantum-enhanced pre-trained BERT model for text classification. In particular, our proposed hybrid classical-quantum model consists of a novel random quantum temporal convolution (QTC) learning framework replacing some layers in the BERT-based decoder. Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets. Particularly, the BERT-QTC boosts the performance of the existing quantum circuit-based LLM in two text classification datasets by 1.57% and 1.52% relative improvements. Furthermore, BERT-QTC can be feasibly deployed on both existing commercial-accessible quantum computation hardware and CPU-based interface for ensuring data isolation.