Computationally efficient Bayesian quantum state tomography

Joseph M. Lukens; Kody J.H. Law; Ajay Jasra; Pavel Lougovski

doi:10.1109/IPC47351.2020.9252416

Computationally efficient Bayesian quantum state tomography

Joseph M. Lukens, Kody J.H. Law, Ajay Jasra, Pavel Lougovski

Computer, Electrical and Mathematical Sciences and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We describe a method for Bayesian quantum state estimation combining efficient parameterization, a pseudo-likelihood, and advanced numerical sampling techniques. Examples reveal significant computational speedup, indicating the approach's promise in practical quantum state tomography.

Original language	English (US)
Title of host publication	2020 IEEE Photonics Conference (IPC)
Publisher	IEEE
ISBN (Print)	9781728158914
DOIs	https://doi.org/10.1109/IPC47351.2020.9252416
State	Published - Sep 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-12-28
Acknowledgements: We thank R. S. Bennink and B. P. Williams for discussions. This work was performed in part at Oak Ridge National Laboratory, operated by UT-Battelle for the U.S. Department of Energy under contract no. DE-AC0500OR22725. Funding was provided by the U.S. Department of Energy, Office of Advanced Scientific Computing Research, through the Quantum Algorithm Teams and Early Career Research Programs.

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10.1109/IPC47351.2020.9252416

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@inproceedings{f0b990bcdd5147c4baf25f7a2435ba23,

title = "Computationally efficient Bayesian quantum state tomography",

abstract = "We describe a method for Bayesian quantum state estimation combining efficient parameterization, a pseudo-likelihood, and advanced numerical sampling techniques. Examples reveal significant computational speedup, indicating the approach's promise in practical quantum state tomography.",

author = "Lukens, {Joseph M.} and Law, {Kody J.H.} and Ajay Jasra and Pavel Lougovski",

note = "KAUST Repository Item: Exported on 2020-12-28 Acknowledgements: We thank R. S. Bennink and B. P. Williams for discussions. This work was performed in part at Oak Ridge National Laboratory, operated by UT-Battelle for the U.S. Department of Energy under contract no. DE-AC0500OR22725. Funding was provided by the U.S. Department of Energy, Office of Advanced Scientific Computing Research, through the Quantum Algorithm Teams and Early Career Research Programs.",

year = "2020",

month = sep,

doi = "10.1109/IPC47351.2020.9252416",

language = "English (US)",

isbn = "9781728158914",

booktitle = "2020 IEEE Photonics Conference (IPC)",

publisher = "IEEE",

}

TY - GEN

T1 - Computationally efficient Bayesian quantum state tomography

AU - Lukens, Joseph M.

AU - Law, Kody J.H.

AU - Jasra, Ajay

AU - Lougovski, Pavel

N1 - KAUST Repository Item: Exported on 2020-12-28 Acknowledgements: We thank R. S. Bennink and B. P. Williams for discussions. This work was performed in part at Oak Ridge National Laboratory, operated by UT-Battelle for the U.S. Department of Energy under contract no. DE-AC0500OR22725. Funding was provided by the U.S. Department of Energy, Office of Advanced Scientific Computing Research, through the Quantum Algorithm Teams and Early Career Research Programs.

PY - 2020/9

Y1 - 2020/9

N2 - We describe a method for Bayesian quantum state estimation combining efficient parameterization, a pseudo-likelihood, and advanced numerical sampling techniques. Examples reveal significant computational speedup, indicating the approach's promise in practical quantum state tomography.

AB - We describe a method for Bayesian quantum state estimation combining efficient parameterization, a pseudo-likelihood, and advanced numerical sampling techniques. Examples reveal significant computational speedup, indicating the approach's promise in practical quantum state tomography.

UR - http://hdl.handle.net/10754/666658

UR - https://ieeexplore.ieee.org/document/9252416/

UR - http://www.scopus.com/inward/record.url?scp=85097863801&partnerID=8YFLogxK

U2 - 10.1109/IPC47351.2020.9252416

DO - 10.1109/IPC47351.2020.9252416

M3 - Conference contribution

SN - 9781728158914

BT - 2020 IEEE Photonics Conference (IPC)

PB - IEEE

ER -

Computationally efficient Bayesian quantum state tomography

Abstract

Bibliographical note

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