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arXiv · 2502.13090

tn4ml: Tensor Network Training and Customization for Machine Learning

Ema Puljak·Sergio Sanchez-Ramirez·Sergi Masot-Llima·Jofre Vallès-Muns·Artur Garcia-Saez·Maurizio Pierini
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Abstract

Tensor Networks have emerged as a prominent alternative to neural networks for addressing Machine Learning challenges in foundational sciences, paving the way for their applications to real-life problems. This paper introduces tn4ml, a novel library designed to seamlessly integrate Tensor Networks into optimization pipelines for Machine Learning tasks. Inspired by existing Machine Learning frameworks, the library offers a user-friendly structure with modules for data embedding, objective function definition, and model training using diverse optimization strategies. We demonstrate its versatility through two examples: supervised learning on tabular data and unsupervised learning on an image dataset. Additionally, we analyze how customizing the parts of the Machine Learning pipeline for Tensor Networks influences performance metrics.

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Ema Puljak, Sergio Sanchez-Ramirez, Sergi Masot-Llima, Jofre Vallès-Muns, Artur Garcia-Saez, Maurizio Pierini. 2025-02-18. tn4ml: Tensor Network Training and Customization for Machine Learning. https://arxiv.org/abs/2502.13090

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