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research article
From tensor-network quantum states to tensorial recurrent neural networks
July 5, 2023
We show that any matrix product state (MPS) can be exactly represented by a recurrent neural network (RNN) with a linear memory update. We generalize this RNN architecture to two-dimensional lattices using a multilinear memory update. It supports perfect sampling and wave-function evaluation in polynomial time, and can represent an area law of entanglement entropy. Numerical evidence shows that it can encode the wave function using a bond dimension lower by orders of magnitude when compared to MPS, with an accuracy that can be systematically improved by increasing the bond dimension.
Type
research article
Web of Science ID
WOS:001050226900002
Authors
Publication date
2023-07-05
Publisher
Published in
Volume
5
Issue
3
Article Number
L032001
Subjects
Peer reviewed
REVIEWED
EPFL units
Available on Infoscience
August 28, 2023
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