Google AI introduced TensorNetwork – an open-source library for efficient calculations for tensor networks. In the blog post, Chase Roberts, Research Engineer at Google AI and Stefan Leichenauer, Research Scientist at X, write about the need of such a library in order to bridge the gap between Tensor Networks and Machine Learning.
Tensor Networks, a data structure that is less known (at least in the machine learning community) allows performing efficient computations in quantum physics, where quantum states can become exponentially large.
Arguing that tensor networks can be used in machine learning and that this data structure has already been finding some applications within ML, researchers point out the need for a tensor networks library.
Engineers and researchers from Google as well as the Perimeter Institute for Theoretical Physics and X, have developed an efficient library that will allow running tensor network algorithms at scale.
The novel library, named TensorNetwork uses TensorFlow as backend and provides significant speedups, especially because it allows the usage of GPUs.
Together with the library, researchers are planning to release a series of papers which will describe the library, and provide example use-cases within physics as well as within machine learning.
In the first paper, that is actually released researchers introduce the library and its API and give an overview of tensor networks for the non-physics audience.