  While quantum architectures are still under development, when available, they
will only be able to process quantum data when machine learning algorithms can
only process numerical data. Therefore, in the issues of classification or
regression, it is necessary to simulate and study quantum systems that will
transfer the numerical input data to a quantum form and enable quantum
computers to use the available methods of machine learning. This material
includes the results of experiments on training and performance of a hybrid
quantum-classical neural network developed for the problem of classification of
handwritten digits from the MNIST data set. The comparative results of two
models: classical and quantum neural networks of a similar number of training
parameters, indicate that the quantum network, although its simulation is
time-consuming, overcomes the classical network (it has better convergence and
achieves higher training and testing accuracy).
