Entangling Machine Learning with Quantum Tensor Networks
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911804299411456 |
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| author | van der Poel, Constantijn Zhao, Dan |
| author_facet | van der Poel, Constantijn Zhao, Dan |
| contents | This paper examines the use of tensor networks, which can efficiently represent high-dimensional quantum states, in language modeling. It is a distillation and continuation of the work done in (van der Poel, 2023). To do so, we will abstract the problem down to modeling Motzkin spin chains, which exhibit long-range correlations reminiscent of those found in language. The Matrix Product State (MPS), also known as the tensor train, has a bond dimension which scales as the length of the sequence it models. To combat this, we use the factored core MPS, whose bond dimension scales sub-linearly. We find that the tensor models reach near perfect classifying ability, and maintain a stable level of performance as the number of valid training examples is decreased. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12969 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Entangling Machine Learning with Quantum Tensor Networks van der Poel, Constantijn Zhao, Dan Machine Learning Quantum Physics This paper examines the use of tensor networks, which can efficiently represent high-dimensional quantum states, in language modeling. It is a distillation and continuation of the work done in (van der Poel, 2023). To do so, we will abstract the problem down to modeling Motzkin spin chains, which exhibit long-range correlations reminiscent of those found in language. The Matrix Product State (MPS), also known as the tensor train, has a bond dimension which scales as the length of the sequence it models. To combat this, we use the factored core MPS, whose bond dimension scales sub-linearly. We find that the tensor models reach near perfect classifying ability, and maintain a stable level of performance as the number of valid training examples is decreased. |
| title | Entangling Machine Learning with Quantum Tensor Networks |
| topic | Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2403.12969 |