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 -