Quantum integration in swin transformer mitigates overfitting in breast cancer screening

Abstract

Abstract To explore the potential of quantum computing in advancing transformer-based deep learning models for breast cancer screening, this study introduces the Quantum-Enhanced Swin Transformer (QEST). This model integrates a Variational Quantum Circuit (VQC) to replace the fully connected layer responsible for classification in the Swin Transformer architecture. In simulations, QEST exhibited competitive accuracy and generalization performance compared to the original Swin Transformer, while also demonstrating an effect in mitigating overfitting. Specifically, in 16-qubit simulations, the VQC reduced the parameter count by 62.5% compared with the replaced fully connected layer and improved the Balanced Accuracy (BACC) by 3.62% in external validation. Furthermore, validation experiments conducted on an actual quantum computer have corroborated the effectiveness of QEST.