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Article: Quantum Similarity Testing with Convolutional Neural Networks

TitleQuantum Similarity Testing with Convolutional Neural Networks
Authors
Issue Date22-May-2023
PublisherAmerican Physical Society
Citation
Physical Review Letters, 2023, v. 130, n. 21 How to Cite?
Abstract

The task of testing whether two uncharacterized quantum devices behave in the same way is crucial for benchmarking near-term quantum computers and quantum simulators, but has so far remained open for continuous variable quantum systems. In this Letter, we develop a machine learning algorithm for comparing unknown continuous variable states using limited and noisy data. The algorithm works on nonGaussian quantum states for which similarity testing could not be achieved with previous techniques. Our approach is based on a convolutional neural network that assesses the similarity of quantum states based on a lower-dimensional state representation built from measurement data. The network can be trained off-line with classically simulated data from a fiducial set of states sharing structural similarities with the states to be tested, with experimental data generated by measurements on the fiducial states, or with a combination of simulated and experimental data. We test the performance of the model on noisy cat states and states generated by arbitrary selective number-dependent phase gates. Our network can also be applied to the problem of comparing continuous variable states across different experimental platforms, with different sets of achievable measurements, and to the problem of experimentally testing whether two states are equivalent up to Gaussian unitary transformations.


Persistent Identifierhttp://hdl.handle.net/10722/331296
ISSN
2023 Impact Factor: 8.1
2023 SCImago Journal Rankings: 3.040
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWu, YD-
dc.contributor.authorZhu, Y-
dc.contributor.authorBai, G-
dc.contributor.authorWang, YX-
dc.contributor.authorChiribella, G-
dc.date.accessioned2023-09-21T06:54:27Z-
dc.date.available2023-09-21T06:54:27Z-
dc.date.issued2023-05-22-
dc.identifier.citationPhysical Review Letters, 2023, v. 130, n. 21-
dc.identifier.issn0031-9007-
dc.identifier.urihttp://hdl.handle.net/10722/331296-
dc.description.abstract<p></p><p>The task of testing whether two uncharacterized quantum devices behave in the same way is crucial for benchmarking near-term quantum computers and quantum simulators, but has so far remained open for continuous variable quantum systems. In this Letter, we develop a machine learning algorithm for comparing unknown continuous variable states using limited and noisy data. The algorithm works on nonGaussian quantum states for which similarity testing could not be achieved with previous techniques. Our approach is based on a convolutional neural network that assesses the similarity of quantum states based on a lower-dimensional state representation built from measurement data. The network can be trained off-line with classically simulated data from a fiducial set of states sharing structural similarities with the states to be tested, with experimental data generated by measurements on the fiducial states, or with a combination of simulated and experimental data. We test the performance of the model on noisy cat states and states generated by arbitrary selective number-dependent phase gates. Our network can also be applied to the problem of comparing continuous variable states across different experimental platforms, with different sets of achievable measurements, and to the problem of experimentally testing whether two states are equivalent up to Gaussian unitary transformations.<br></p>-
dc.languageeng-
dc.publisherAmerican Physical Society-
dc.relation.ispartofPhysical Review Letters-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleQuantum Similarity Testing with Convolutional Neural Networks-
dc.typeArticle-
dc.identifier.doi10.1103/PhysRevLett.130.210601-
dc.identifier.scopuseid_2-s2.0-85161276057-
dc.identifier.volume130-
dc.identifier.issue21-
dc.identifier.eissn1079-7114-
dc.identifier.isiWOS:001004221400003-
dc.identifier.issnl0031-9007-

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