Entangling Machine Learning with Quantum Tensor Networks
Constantijn van der Poel Dan Zhao
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.