Sample-Efficient Adaptive Calibration of Quantum Networks Using Bayesian Optimization-Reference-Cited by-同舟云学术

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Sample-Efficient Adaptive Calibration of Quantum Networks Using Bayesian Optimization

Published:2022-03-28 Issue:3 Volume:17 Page:
ISSN:2331-7019
Container-title:Physical Review Applied
language:en
Short-container-title:Phys. Rev. Applied

Author:

Cortes Cristian L.,Lefebvre Pascal^ORCID,Lauk Nikolai^ORCID,Davis Michael J.^ORCID,Sinclair Neil,Gray Stephen K.^ORCID,Oblak Daniel

Funder

U.S. Department of Energy

Office of Science

Basic Energy Sciences

Division of Chemical Sciences, Geosciences, and Biosciences

Natural Sciences and Engineering Research Council of Canada

Publisher

American Physical Society (APS)

Subject

General Physics and Astronomy

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevApplied.17.034067/fulltext

Reference65 articles.

1. Solving the quantum many-body problem with artificial neural networks

2. Supervised learning with quantum-enhanced feature spaces

3. Programmable photonic circuits

4. Self-verifying variational quantum simulation of lattice models

5. Solving a Higgs optimization problem with quantum annealing for machine learning

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1. Cookie-Jar: An Adaptive Re-configurable Framework for Wireless Network Infrastructures;Proceedings of the 21st ACM International Conference on Computing Frontiers;2024-05-07

2. Two-photon quantum state tomography of photonic qubits;Physical Review A;2024-02-02

3. Development of a Boston-area 50-km fiber quantum network testbed;Physical Review Applied;2024-01-16

4. Preparing quantum states by measurement-feedback control with Bayesian optimization;Frontiers of Physics;2023-07-01

5. Non-local polarization alignment and control in fibers using feedback from correlated measurements of entangled photons;Optics Express;2023-01-10

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