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Optimization schemes for unitary tensor-network circuit

Haghshenas, Reza (2021) Optimization schemes for unitary tensor-network circuit. Physical Review Research, 3 (2). Art. No. 023148. ISSN 2643-1564. doi:10.1103/PhysRevResearch.3.023148. https://resolver.caltech.edu/CaltechAUTHORS:20200928-121032434

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Abstract

An efficient representation of a quantum circuit is of great importance in achieving a quantum advantage on current noisy intermediate scale quantum (NISQ) devices and the classical simulation of quantum many-body systems. The quantum circuits are playing the key ingredient in the performance of variational quantum algorithms and quantum dynamics in problems of physics and chemistry. In this paper, we study the role of the network structure of a quantum circuit in its performance. We discuss the variational optimization of quantum circuit (a unitary tensor-network circuit) with different network structures. The ansatz is performed based on a generalization of well-developed multiscale entanglement renormalization algorithm and also the conjugate-gradient method with an effective line search. We present the benchmarking calculations for different network structures by studying the Heisenberg model in a strongly disordered magnetic field and a tensor-network QR decomposition. Our work can contribute to achieve the most out of NISQ hardware and to classically develop isometric tensor network states.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1103/PhysRevResearch.3.023148DOIArticle
https://arxiv.org/abs/2009.02606arXivDiscussion Paper
ORCID:
AuthorORCID
Haghshenas, Reza0000-0002-5593-8915
Alternate Title:Optimization schemes for unitary tensor-network operator
Additional Information:© 2021 Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Received 21 September 2020; revised 24 March 2021; accepted 4 May 2021; published 26 May 2021. This work was supported by the US Department of Energy, Office of Science, via award no DE-SC0019374. We have used library Uni10 [59] to perform tensor-network ansatz. We thank F. Pollmann for the initial ideas of this work and also thank D. N. Sheng, A. Langari, A. T. Rezakhani, and G. K. Chan for helpful discussions. We appreciate F. Pollmann and P. Helms for their useful comments and for reading the manuscript.
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0019374
Issue or Number:2
DOI:10.1103/PhysRevResearch.3.023148
Record Number:CaltechAUTHORS:20200928-121032434
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200928-121032434
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105583
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:28 Sep 2020 19:46
Last Modified:26 May 2021 22:19

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