Entangling Machine Learning with Quantum Tensor Networks

Constantijn van der Poel, Dan Zhao
1/9/2024
cs.LGquant-ph

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Abstract

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.

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Cite this paper

@article{poel2024entangling,
  title  = {Entangling Machine Learning with Quantum Tensor Networks},
  author = {Constantijn van der Poel and Dan Zhao},
  year   = {2024},
  eprint = {2403.12969},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.12969v1}
}

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