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arXiv:1905.01330

TensorNetwork: A Library for Physics and Machine Learning

Chase Roberts, Ashley Milsted, Martin Ganahl, Adam Zalcman, Fontaine, Bruce, Yijian Zou, Jack D. Hidary, Guifré Vidal, Stefan Leichenauer

Published May 3, 2019• 66 citations
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Abstract

TensorNetwork is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body physics, but are currently also applied in a number of other research areas, including machine learning. We demonstrate the use of the API with applications both physics and machine learning, with details appearing in companion papers.

DOI
10.48550/arxiv.1905.01330

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Tensor networks are factorizations of high-dimensional tensors into networks of smaller tensors. They have applications in physics and mathematics, and recently have been proposed as promising machine learning architectures. To ease the integration of tensor networks in machine learning pipelines, we introduce TensorKrowch, an open source Python library built on top of PyTorch. Providing a user-friendly interface, TensorKrowch allows users to construct any tensor network, train it, and integrate it as a layer in more intricate deep learning models. In this paper, we describe the main functionality and basic usage of TensorKrowch, and provide technical details on its building blocks and the optimizations performed to achieve efficient operation.
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