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PhysRevApplied.15.034073.pdf
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Final Published Version
Affiliation
Wyant College of Optical Sciences, University of ArizonaDepartment of Electrical and Computer Engineering, University of Arizona
Issue Date
2021-03-25
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American Physical Society (APS)Citation
Wu, J., & Zhuang, Q. (2021). Continuous-variable error correction for general Gaussian noises. Physical Review Applied, 15(3), 034073.Journal
Physical Review AppliedRights
© 2021 authors. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Quantum error correction is essential for robust quantum-information processing with noisy devices. As bosonic quantum systems play a role in quantum sensing, communication, and computation, it is useful to design error-correction codes suitable for these systems against various different types of noises. While most efforts aim at protecting qubits encoded into the infinite-dimensional Hilbert space of a bosonic mode, Ref. [Phys. Rev. Lett. 125, 080503 (2020)] proposed an error-correction code to maintain the infinite-dimensional-Hilbert-space nature of bosonic systems by encoding a single bosonic mode into multiple bosonic modes. Enabled by Gottesman-Kitaev-Preskill states as ancilla, the code overcomes the no-go theorem of Gaussian error correction. In this work, we generalize the error-correction code to the scenario with general correlated and heterogeneous Gaussian noises, including memory effects. We introduce Gaussian preprocessing and postprocessing to convert the general noise model to an independent but heterogeneous collection of additive white Gaussian noise channels and then apply concatenated codes in an optimized manner. To evaluate the performance, we develop a theory framework to enable the efficient calculation of the noise SD after the error correction, despite the non-Gaussian nature of the codes. Our code provides the optimal scaling of the residue-noise SD with the number of modes and can be widely applied to distributed sensor networks, network communication, and composite quantum-memory systems. © 2021 authors. Published by the American Physical Society.Note
Open access articleEISSN
2331-7019Version
Final published versionSponsors
Defense Advanced Research Projects Agencyae974a485f413a2113503eed53cd6c53
10.1103/physrevapplied.15.034073
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Except where otherwise noted, this item's license is described as © 2021 authors. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license.