1Departamento de Análisis Matemático, Universidad Complutense de Madrid, 28040 Madrid, Spain
2Instituto de Ciencias Matemáticas (CSIC-UAM-UC3M-UCM), 28049 Madrid, Spain
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Abstract
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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Please also see Tensor Networks with PyTorch at github.
Popular summary
This work introduces TensorKrowch, a software library for building and training machine learning models based on tensor networks. It is built on top of the popular library PyTorch, which ensures seamless integration with standard machine learning pipelines.
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