Comparing concepts of quantum and classical neural network models for image classification task
This work addresses the problem of enabling quantum computers to process classical data for machine learning tasks, though it is incremental as it focuses on simulation and comparison rather than real quantum hardware.
The paper tackles the challenge of adapting classical image classification tasks for quantum computers by developing a hybrid quantum-classical neural network for MNIST digit classification, achieving better convergence and higher training and testing accuracy compared to a classical network with a similar number of parameters.
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).