Journal article
Neural networks for quantum inverse problems
- Abstract:
- Quantum inverse problem (QIP) is the problem of estimating an unknown quantum system from a set of measurements, whereas the classical counterpart is the inverse problem of estimating a distribution from a set of observations. In this paper, we present a neural-network-based method for QIPs, which has been widely explored for its classical counterpart. The proposed method utilizes the quantumness of the QIPs and takes advantage of the computational power of neural networks to achieve remarkable efficiency for the quantum state estimation. We test the method on the problem of maximum entropy estimation of an unknown state ρ from partial information both numerically and experimentally. Our method yields high fidelity, efficiency and robustness for both numerical experiments and quantum optical experiments.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.6MB, Terms of use)
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- Publisher copy:
- 10.1088/1367-2630/ac706c
Authors
+ National Natural Science Foundation of China
More from this funder
- Funder identifier:
- https://ror.org/01h0zpd94
- Publisher:
- IOP Publishing
- Journal:
- New Journal of Physics More from this journal
- Volume:
- 24
- Issue:
- 6
- Article number:
- 063002
- Publication date:
- 2022-06-06
- Acceptance date:
- 2022-05-17
- DOI:
- EISSN:
-
1367-2630
- Language:
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English
- Keywords:
- Pubs id:
-
2021634
- Local pid:
-
pubs:2021634
- Deposit date:
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2025-02-12
Terms of use
- Copyright holder:
- Cao et al
- Copyright date:
- 2022
- Rights statement:
- © 2022 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
- Licence:
- CC Attribution (CC BY)
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