Analysis of Influencing Factors in Quantum Neural Network for Solving Classification Problem

In recent years, the research verification and application exploration of quantum algorithms have developed more rapidly. Quantum algorithm exploration and verification based on quantum computing cloud platform has become one of the important ways. Quantum Neural Network (QNN) combines quantum computing theory with artificial neural network, which is expected to provide potential solutions for classification problems and so on. In the algorithm execution process, factors such as encoding layer, ansatz, and optimizer have all influenced the results. Based on the quantum simulator provided on the quantum computing cloud platform, QNN has been used for experimental validation to study the effects of encoding methods, ansatz types and optimizer variations on the accuracy of QNN training results. Additionally, this artical provides insights into the future trends of quantum algorithm development.