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While quantum architectures are still under development, when available, theywill only be able to process quantum data when machine learning algorithms canonly process numerical data. Therefore, in the issues of classification orregression, it is necessary to simulate and study quantum systems that willtransfer the numerical input data to a quantum form and enable quantumcomputers to use the available methods of machine learning. This materialincludes the results of experiments on training and performance of a hybridquantum-classical neural network developed for the problem of classification ofhandwritten digits from the MNIST data set. The comparative results of twomodels: classical and quantum neural networks of a similar number of trainingparameters, indicate that the quantum network, although its simulation istime-consuming, overcomes the classical network (it has better convergence andachieves higher training and testing accuracy).

Comparing concepts of quantum and classical neural network models for image classification task