Entangling Machine Learning with Quantum Tensor Networks

arXiv:2403.12969v1 Announce Type: new
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 …

abstract arxiv cs.lg distillation language machine machine learning modeling networks paper quant-ph quantum spin tensor type van will work