Quantum assisted unsupervised data clustering on the basis of neural networks

Recent progress in machine learning has affected almost all areas of the modern economy. The use of quantum protocols to speed up classical machine learning approaches may have further profound effects on society in the future. Here, we developed a hybrid quantum-assisted self-organizing feature map, a type of artificial neural network, and apply it to the data clustering problem in an unsupervised manner. We show that it allows us to reduce the number of calculations in a number of clusters. It is believed that similar types of hybrid quantum classical algorithms can be the main test bed to achieve practical quantum supremacy on Noisy Intermediate Scale Quantum devices.