Benchmarking quantum tomography completeness and fidelity with machine learning

Benchmarking quantum tomography completeness and fidelity with machine learning

We train convolutional neural networks to predict whether or not a set of measurements is informationally complete to uniquely reconstruct any given quantum state with no prior information. In addition, we perform fidelity benchmarking based on this measurement set without explicitly carrying out st...

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Bibliographic Details
Main Authors: Yong Siah Teo, Seongwook Shin, Hyunseok Jeong, Yosep Kim, Yoon-Ho Kim, Gleb I Struchalin, Egor V Kovlakov, Stanislav S Straupe, Sergei P Kulik, Gerd Leuchs, Luis L Sánchez-Soto
Format: Article
Language:English
Published: IOP Publishing 2021-01-01
Series:New Journal of Physics
Subjects:
quantum tomography
convolutional networks
compressive
Online Access:https://doi.org/10.1088/1367-2630/ac1fcb

Internet

https://doi.org/10.1088/1367-2630/ac1fcb

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