Entangling Machine Learning with Quantum Tensor Networks
<|reference_start|>Entangling Machine Learning with Quantum Tensor Networks: 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.<|reference_end|>
@article{van der poel2024entangling,
    title={Entangling Machine Learning with Quantum Tensor Networks},
    author={Constantijn van der Poel, Dan Zhao},
    journal={arXiv preprint arXiv:2403.12969},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12969},
    primaryClass={cs.LG quant-ph}
}