Abstract
AbstractThe 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.