Quantum neural network classifier with differential privacy

Abstract Quantum neural networks (QNN) hold the potential for quantum advantages but are confronted with privacy leakage risks that could undermine users’ benefits. To address this issue, we propose an enhanced QNN model integrated with differential privacy. After converting classical data into quantum states using encoding circuit, we apply data re-uploading technology to embed the encoding circuit multiple times into the overall circuit. The classification outcomes are derived by measuring the final quantum state. During the optimization process of QNN, we introduce Gaussian noise into the gradient of the loss function to ensure differential privacy. Our theoretical analysis demonstrates that in differential privacy settings, the model effectively protects privacy while demonstrating good performance. Our experimental analysis demonstrates that on four standard datasets, our model achieves the same accuracy as classical neural network (NN) while utilizing only 22% of the parameters of NN, and exhibiting lower performance loss compared to other QNN.