RT Journal Article
T1 Objective compressive quantum process tomography
A1 Teo, Yong Siah
A1 Struchalin, G. I.
A1 Kovlakov, E. V.
A1 Ahn, Daekun
A1 Jeong, Hyunseok
A1 Straupe, S. S.
A1 Kulik, S. P.
A1 Leuchs, Gerd
A1 Sánchez Soto, Luis Lorenzo
AB We present a compressive quantum process tomography scheme that fully characterizes any rank-deficient completely positive process with no spurious a priori information. It uses randomly chosen input states and adaptive output von Neumann measurements. Both entangled and tensor-product configurations are flexibly employable in our scheme, the latter of which are especially compatible with many-body quantum computing. Two main features of this scheme are the certification protocol that verifies whether the accumulated data uniquely characterize the quantum process and a compressive reconstruction method for the output states. We emulate multipartite scenarios with high-order transverse modes and optical fibers to demonstrate that, in terms of measurement resources, our assumption-free compressive strategy can reconstruct quantum processes almost equally efficiently using all types of input states and basis measurements.
PB Amer Physical Soc
SN 2469-9926
YR 2020
FD 2020-02-01
LK https://hdl.handle.net/20.500.14352/6114
UL https://hdl.handle.net/20.500.14352/6114
LA eng
NO ©2020 American Physical Society.We acknowledge financial support from the BK21 Plus Program (No. 21A20131111123) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF), the NRF grant funded by the Korea government (MSIP) (Grants No. NRF-2019M3E4A1080074, No. NRF-2019R1H1A3079890, and No. NRF-2018K2A9A1A06069933), Russian Foundation for Basic Research (RFBR Projects No. 19-32-80043 and No. 19-52-80034), Mega-grant of the Ministry of Education and Science of the Russian Federation (Contract No. 14.W03.31.0032), and the Spanish MINECO (Grants No. FIS2015-67963-P and No. PGC2018-099183-B-I00).
NO Ministerio de Economía y Competitividad (MINECO)
DS Docta Complutense
RD 27 abr 2024