GuiTeNet: A graphical user interface for tensor networks

Abstract

We introduce a graphical user interface for constructing arbitrary tensor networks and specifying common operations like contractions or splitting, denoted GuiTeNet. Tensors are represented as nodes with attached legs, corresponding to the ordered dimensions of the tensor. GuiTeNet visualizes the current network, and instantly generates Python/NumPy source code for the hitherto sequence of user actions. Support for additional programming languages is planned for the future. We discuss the elementary operations on tensor networks used by GuiTeNet, together with high-level optimization strategies. The software runs directly in web browsers and is available online at http://guitenet.org.

arXiv comment7 pages, 4 figures

References in corpus (9)

• The NumPy array: a structure for efficient numerical computation
• The density-matrix renormalization group in the age of matrix product states
• Matrix Product States, Projected Entangled Pair States, and variational renormalization group methods for quantum spin systems
• A class of quantum many-body states that can be efficiently simulated
• High-Performance Tensor Contraction without Transposition
• Faster identification of optimal contraction sequences for tensor networks
• Improving the efficiency of variational tensor network algorithms
• NCON: A tensor network contractor for MATLAB
• TensorTrace: an application to contract tensor networks