Intelligent Generative Models for Quantum Neural Networks

The potential of quantum neural networks (QNN) for applications in several fields has been widely demonstrated, but hyperparameter selection remains a technical challenge. The complexity of combinatorial optimization rises dramatically as the dimensionality of the hyperparameter space increases, making hyperparameter selection exceptionally complex. Traditional manual trial-and-error methods are both time-consuming and expensive. To break through this bottleneck, the Ansatz structure set is proposed and the Hyperband algorithm is utilized to combine the structure set with the stacking number and algorithmic hyperparameters to propose the Hyperband-QNN model. The model can intelligently generate QNN according to the actual data features, and in practical applications in many fields such as medicine and agriculture, it performs well compared with the traditional machine learning algorithms as well as the quantum algorithms QSVM and QKNN, and when it is used to deal with the large-scale data, the efficiency advantage becomes more obvious with the increase of the number of samples. This not only proves the advantages of Hyperband-QNN model in practical applications but also provides useful exploration and reference for the integration of traditional algorithms and quantum algorithms in practical problems and the play of their respective advantages.