Lightweight Quantum Neural Networks Intelligent Generation

The decoherence effect of the real quantum computer poses a significant challenge for designing quantum neural networks (QNN), which requires simultaneous consideration of model performances and parameterized quantum circuit (PQC) scales. In this paper, a method for the intelligent generation of lightweight QNN is proposed, which utilizes the parameters of quantum chips such as topology and relaxation time to assist the automatic design of QNNs. Combining the mixture expert mechanism with the Hyperband algorithm, a Mixture of Hyperband Experts for QNN is proposed to search for combinations of hyperparameters that meet multi‐objective requirements from the lightweight QNN. To the best of the authors’ knowledge, this is the first work that combines quantum chip capabilities to intelligently generate lightweight QNNs. Experiments are carried out on datasets in the fields of medicine and information security, and compared with classical machine learning algorithms and quantum machine learning algorithms (QSVM, QKNN, Intelligent Generation for QNN), the QNN automatically generated by this method has good performance and scalability, and the PQC has a smaller scale. The anti‐coherence capability of the QNN intelligently generated by this method is verified using experiments on a real quantum computer.