Entangling Machine Learning with Quantum Tensor Networks

• URL: http://arxiv.org/abs/2403.12969v1
• Date: Tue, 9 Jan 2024 00:07:36 GMT
• Title: Entangling Machine Learning with Quantum Tensor Networks
• Authors: Constantijn van der Poel, Dan Zhao,
• Abstract summary: This paper examines the use of tensor networks, which can efficiently represent high-dimensional quantum states, in language modeling. We will abstract the problem down to modeling Motzkin spin chains, which exhibit long-range correlations reminiscent of those found in language. 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.
• Score: 13.609946378021016
• License: http://creativecommons.org/licenses/by/4.0/
• 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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