Accuracy of a tensor network-based image classifier saturates with bond dimension

In the field of physics, tensor networks have been successfully applied to solve for problems requiring high-dimensionality. As data required in data science are also high-dimensional, tensor methods have also gained appreciation as a tool in machine learning. In this study, we have utilized an existing tensor network-based machine learning program to investigate on the role of bond dimension in the learning rate and the learning accuracy in an image classification problem.