Benchmarking quantum tomography completeness and fidelity with machine learning
Teo YS, Shin S, Jeong H, Kim Y, Kim YH, Struchalin G, Kovlakov E, Straupe SS, Kulik SP, Leuchs G, Sanchez-Soto LL (2021)
Publication Type: Journal article
Publication year: 2021
Journal
Book Volume: 23
Article Number: 103021
Journal Issue: 10
Abstract
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 state tomography. The networks are trained to recognize the fidelity and a reliable measure for informational completeness. By gradually accumulating measurements and data, these trained convolutional networks can efficiently establish a compressive quantum-state characterization scheme by accelerating runtime computation and greatly reducing systematic drifts in experiments. We confirm the potential of this machine-learning approach by presenting experimental results for both spatial-mode and multiphoton systems of large dimensions. These predictions are further shown to improve when the networks are trained with additional bootstrapped training sets from real experimental data. Using a realistic beam-profile displacement error model for Hermite-Gaussian sources, we further demonstrate numerically that the orders-of-magnitude reduction in certification time with trained networks greatly increases the computation yield of a large-scale quantum processor using these sources, before state fidelity deteriorates significantly.
Authors with CRIS profile
Involved external institutions
Max-Planck-Institut für die Physik des Lichts (MPL) / Max Planck Institute for the Science of Light
Germany (DE)
Pohang University of Science and Technology (POSTECH)
Korea, Republic of (KR)
Lomonosov Moscow State University / Московский государственный университет имени М.В.Ломоносова
Russian Federation (RU)
Seoul National University (SNU) / 서울대학교
Korea, Republic of (KR)
Germany (DE)
Pohang University of Science and Technology (POSTECH)
Korea, Republic of (KR)
Lomonosov Moscow State University / Московский государственный университет имени М.В.Ломоносова
Russian Federation (RU)
Seoul National University (SNU) / 서울대학교
Korea, Republic of (KR)
How to cite
APA:
Teo, Y.S., Shin, S., Jeong, H., Kim, Y., Kim, Y.-H., Struchalin, G.,... Sanchez-Soto, L.L. (2021). Benchmarking quantum tomography completeness and fidelity with machine learning. New Journal of Physics, 23(10). https://doi.org/10.1088/1367-2630/ac1fcb
MLA:
Teo, Yong Siah, et al. "Benchmarking quantum tomography completeness and fidelity with machine learning." New Journal of Physics 23.10 (2021).
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