Machine learning of high dimensional data on a noisy quantum processor

Abstract Quantum kernel methods show promise for accelerating data analysis by efficiently learning relationships between input data points that have been encoded into an exponentially large Hilbert space. While this technique has been used successfully in small-scale experiments on synthetic datase...

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Autores principales:	Evan Peters, João Caldeira, Alan Ho, Stefan Leichenauer, Masoud Mohseni, Hartmut Neven, Panagiotis Spentzouris, Doug Strain, Gabriel N. Perdue
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/68962ca062724054a7cdabe34f6649a1
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spelling	oai:doaj.org-article:68962ca062724054a7cdabe34f6649a12021-11-14T12:15:42ZMachine learning of high dimensional data on a noisy quantum processor10.1038/s41534-021-00498-92056-6387https://doaj.org/article/68962ca062724054a7cdabe34f6649a12021-11-01T00:00:00Zhttps://doi.org/10.1038/s41534-021-00498-9https://doaj.org/toc/2056-6387Abstract Quantum kernel methods show promise for accelerating data analysis by efficiently learning relationships between input data points that have been encoded into an exponentially large Hilbert space. While this technique has been used successfully in small-scale experiments on synthetic datasets, the practical challenges of scaling to large circuits on noisy hardware have not been thoroughly addressed. Here, we present our findings from experimentally implementing a quantum kernel classifier on real high-dimensional data taken from the domain of cosmology using Google’s universal quantum processor, Sycamore. We construct a circuit ansatz that preserves kernel magnitudes that typically otherwise vanish due to an exponentially growing Hilbert space, and implement error mitigation specific to the task of computing quantum kernels on near-term hardware. Our experiment utilizes 17 qubits to classify uncompressed 67 dimensional data resulting in classification accuracy on a test set that is comparable to noiseless simulation.Evan PetersJoão CaldeiraAlan HoStefan LeichenauerMasoud MohseniHartmut NevenPanagiotis SpentzourisDoug StrainGabriel N. PerdueNature PortfolioarticlePhysicsQC1-999Electronic computers. Computer scienceQA75.5-76.95ENnpj Quantum Information, Vol 7, Iss 1, Pp 1-5 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Physics QC1-999 Electronic computers. Computer science QA75.5-76.95 Evan Peters João Caldeira Alan Ho Stefan Leichenauer Masoud Mohseni Hartmut Neven Panagiotis Spentzouris Doug Strain Gabriel N. Perdue Machine learning of high dimensional data on a noisy quantum processor
description	Abstract Quantum kernel methods show promise for accelerating data analysis by efficiently learning relationships between input data points that have been encoded into an exponentially large Hilbert space. While this technique has been used successfully in small-scale experiments on synthetic datasets, the practical challenges of scaling to large circuits on noisy hardware have not been thoroughly addressed. Here, we present our findings from experimentally implementing a quantum kernel classifier on real high-dimensional data taken from the domain of cosmology using Google’s universal quantum processor, Sycamore. We construct a circuit ansatz that preserves kernel magnitudes that typically otherwise vanish due to an exponentially growing Hilbert space, and implement error mitigation specific to the task of computing quantum kernels on near-term hardware. Our experiment utilizes 17 qubits to classify uncompressed 67 dimensional data resulting in classification accuracy on a test set that is comparable to noiseless simulation.
format	article
author	Evan Peters João Caldeira Alan Ho Stefan Leichenauer Masoud Mohseni Hartmut Neven Panagiotis Spentzouris Doug Strain Gabriel N. Perdue
author_facet	Evan Peters João Caldeira Alan Ho Stefan Leichenauer Masoud Mohseni Hartmut Neven Panagiotis Spentzouris Doug Strain Gabriel N. Perdue
author_sort	Evan Peters
title	Machine learning of high dimensional data on a noisy quantum processor
title_short	Machine learning of high dimensional data on a noisy quantum processor
title_full	Machine learning of high dimensional data on a noisy quantum processor
title_fullStr	Machine learning of high dimensional data on a noisy quantum processor
title_full_unstemmed	Machine learning of high dimensional data on a noisy quantum processor
title_sort	machine learning of high dimensional data on a noisy quantum processor
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/68962ca062724054a7cdabe34f6649a1
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Machine learning of high dimensional data on a noisy quantum processor

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