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Fast tensor disentangling algorithm

Slagle, Kevin (2021) Fast tensor disentangling algorithm. SciPost Physics, 11 (3). Art. No. 56. ISSN 2542-4653. doi:10.21468/scipostphys.11.3.056.

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Many recent tensor network algorithms apply unitary operators to parts of a tensor network in order to reduce entanglement. However, many of the previously used iterative algorithms to minimize entanglement can be slow. We introduce an approximate, fast, and simple algorithm to optimize disentangling unitary tensors. Our algorithm is asymptotically faster than previous iterative algorithms and often results in a residual entanglement entropy that is within 10 to 40% of the minimum. For certain input tensors, our algorithm returns an optimal solution. When disentangling order-4 tensors with equal bond dimensions, our algorithm achieves an entanglement spectrum where nearly half of the singular values are zero. We further validate our algorithm by showing that it can efficiently disentangle random 1D states of qubits.

Item Type:Article
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URLURL TypeDescription Paper
Slagle, Kevin0000-0002-8036-3447
Additional Information:© 2021 K. Slagle. This work is licensed under the Creative Commons Attribution 4.0 International License. Published by the SciPost Foundation. Received 26-04-2021; Accepted 06-09-2021; Published 13-09-2021. We thank Miles Stoudenmire and Michael Lindsey for helpful discussions and suggestions. Funding information K.S. is supported by the Walter Burke Institute for Theoretical Physics at Caltech; and the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Quantum Science Center.
Group:Institute for Quantum Information and Matter, Walter Burke Institute for Theoretical Physics
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Walter Burke Institute for Theoretical Physics, CaltechUNSPECIFIED
Department of Energy (DOE)UNSPECIFIED
Series Name:SciPost Phys.
Issue or Number:3
Record Number:CaltechAUTHORS:20211116-160842763
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:111890
Deposited By: Tony Diaz
Deposited On:16 Nov 2021 16:51
Last Modified:16 Nov 2021 16:51

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