Quantum-inspired Techniques in Tensor Networks for Industrial Contexts
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910188796116992 |
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| author | Ali, Alejandro Mata Delgado, Iñigo Perez de Leceta, Aitor Moreno Fdez. |
| author_facet | Ali, Alejandro Mata Delgado, Iñigo Perez de Leceta, Aitor Moreno Fdez. |
| contents | In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for industrial environments and contexts, with a compilation of the available literature and an analysis of the use cases that may be affected by such methods. In addition, we explore the limitations of such techniques in order to determine their potential scalability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11277 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Quantum-inspired Techniques in Tensor Networks for Industrial Contexts Ali, Alejandro Mata Delgado, Iñigo Perez de Leceta, Aitor Moreno Fdez. Quantum Physics Emerging Technologies Machine Learning Computational Physics 81P68, 15A69 G.1.3; G.2.1; I.2; I.4 In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for industrial environments and contexts, with a compilation of the available literature and an analysis of the use cases that may be affected by such methods. In addition, we explore the limitations of such techniques in order to determine their potential scalability. |
| title | Quantum-inspired Techniques in Tensor Networks for Industrial Contexts |
| topic | Quantum Physics Emerging Technologies Machine Learning Computational Physics 81P68, 15A69 G.1.3; G.2.1; I.2; I.4 |
| url | https://arxiv.org/abs/2404.11277 |