Visual recognition is currently one of the most important and active research areas in
computer vision, pattern recognition, and even the general field of artificial intelligence. It …

Inspired by coarse-graining approaches used in physics, we show how similar algorithms
can be adapted for data. The resulting algorithms are based on layered tree tensor networks …

Tensor networks have found a wide use in a variety of applications in physics and computer
science, recently leading to both theoretical insights as well as practical algorithms in …

It is a hot topic how entanglement, a quantity from quantum information theory, can assist
machine learning. In this work, we implement numerical experiments to classify …

Tensor networks have found a wide use in a variety of applications in physics and computer
science, recently leading to both theoretical insights as well as practical algorithms in …

We introduce complex-valued tensor network models for sequence processing motivated by
correspondence to probabilistic graphical models, interpretability and resource …

In this paper, we propose a multiscale entanglement renormalization ansatz (MERA) feature
extraction method based on a novel quantum convolutional neural network (QCNN) for …

We propose a restricted class of tensor network state, built from number-state preserving
tensors, for supervised learning tasks. This class of tensor network is argued to be a natural …

Recently it has been shown that tensor networks (TNs) have the ability to represent the
expected return of a single-agent finite Markov decision process (FMDP). The TN represents …