tn4ml: Tensor Network Training and Customization for Machine Learning
Fuente:
arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866929719307403264 |
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| author | Puljak, Ema Sanchez-Ramirez, Sergio Masot-Llima, Sergi Vallès-Muns, Jofre Garcia-Saez, Artur Pierini, Maurizio |
| author_facet | Puljak, Ema Sanchez-Ramirez, Sergio Masot-Llima, Sergi Vallès-Muns, Jofre Garcia-Saez, Artur Pierini, Maurizio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13090 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | tn4ml: Tensor Network Training and Customization for Machine Learning Puljak, Ema Sanchez-Ramirez, Sergio Masot-Llima, Sergi Vallès-Muns, Jofre Garcia-Saez, Artur Pierini, Maurizio Machine Learning Mathematical Software Quantum Physics 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. |
| title | tn4ml: Tensor Network Training and Customization for Machine Learning |
| topic | Machine Learning Mathematical Software Quantum Physics |
| url | https://arxiv.org/abs/2502.13090 |