Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 115 |
| Fachzeitschrift | npj Quantum information |
| Jahrgang | 8 |
| Ausgabenummer | 1 |
| Publikationsstatus | Veröffentlicht - 23 Sept. 2022 |
| Extern publiziert | Ja |
Abstract
Modern day quantum simulators can prepare a wide variety of quantum states but the accurate estimation of observables from tomographic measurement data often poses a challenge. We tackle this problem by developing a quantum state tomography scheme which relies on approximating the probability distribution over the outcomes of an informationally complete measurement in a variational manifold represented by a convolutional neural network. We show an excellent representability of prototypical ground- and steady states with this ansatz using a number of variational parameters that scales polynomially in system size. This compressed representation allows us to reconstruct states with high classical fidelities outperforming standard methods such as maximum likelihood estimation. Furthermore, it achieves a reduction of the estimation error of observables by up to an order of magnitude compared to their direct estimation from experimental data.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Informatik (sonstige)
- Physik und Astronomie (insg.)
- Statistische und nichtlineare Physik
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Theoretische Informatik und Mathematik
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in: npj Quantum information, Jahrgang 8, Nr. 1, 115, 23.09.2022.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Efficient quantum state tomography with convolutional neural networks
AU - Schmale, Tobias
AU - Reh, Moritz
AU - Gärttner, Martin
N1 - Publisher Copyright: © 2022, The Author(s).
PY - 2022/9/23
Y1 - 2022/9/23
N2 - Modern day quantum simulators can prepare a wide variety of quantum states but the accurate estimation of observables from tomographic measurement data often poses a challenge. We tackle this problem by developing a quantum state tomography scheme which relies on approximating the probability distribution over the outcomes of an informationally complete measurement in a variational manifold represented by a convolutional neural network. We show an excellent representability of prototypical ground- and steady states with this ansatz using a number of variational parameters that scales polynomially in system size. This compressed representation allows us to reconstruct states with high classical fidelities outperforming standard methods such as maximum likelihood estimation. Furthermore, it achieves a reduction of the estimation error of observables by up to an order of magnitude compared to their direct estimation from experimental data.
AB - Modern day quantum simulators can prepare a wide variety of quantum states but the accurate estimation of observables from tomographic measurement data often poses a challenge. We tackle this problem by developing a quantum state tomography scheme which relies on approximating the probability distribution over the outcomes of an informationally complete measurement in a variational manifold represented by a convolutional neural network. We show an excellent representability of prototypical ground- and steady states with this ansatz using a number of variational parameters that scales polynomially in system size. This compressed representation allows us to reconstruct states with high classical fidelities outperforming standard methods such as maximum likelihood estimation. Furthermore, it achieves a reduction of the estimation error of observables by up to an order of magnitude compared to their direct estimation from experimental data.
UR - http://www.scopus.com/inward/record.url?scp=85139155193&partnerID=8YFLogxK
U2 - 10.1038/s41534-022-00621-4
DO - 10.1038/s41534-022-00621-4
M3 - Article
AN - SCOPUS:85139155193
VL - 8
JO - npj Quantum information
JF - npj Quantum information
SN - 2056-6387
IS - 1
M1 - 115
ER -