A quantum Hopfield associative memory implemented on an actual quantum processor

Abstract In this work, we present a Quantum Hopfield Associative Memory (QHAM) and demonstrate its capabilities in simulation and hardware using IBM Quantum Experience.. The QHAM is based on a quantum neuron design which can be utilized for many different machine learning applications and can be imp...

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Autores principales:	Nathan Eli Miller, Saibal Mukhopadhyay
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ccb7a068134b4afa8df8a3a851794bb7
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spelling	oai:doaj.org-article:ccb7a068134b4afa8df8a3a851794bb72021-12-05T12:11:29ZA quantum Hopfield associative memory implemented on an actual quantum processor10.1038/s41598-021-02866-z2045-2322https://doaj.org/article/ccb7a068134b4afa8df8a3a851794bb72021-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02866-zhttps://doaj.org/toc/2045-2322Abstract In this work, we present a Quantum Hopfield Associative Memory (QHAM) and demonstrate its capabilities in simulation and hardware using IBM Quantum Experience.. The QHAM is based on a quantum neuron design which can be utilized for many different machine learning applications and can be implemented on real quantum hardware without requiring mid-circuit measurement or reset operations. We analyze the accuracy of the neuron and the full QHAM considering hardware errors via simulation with hardware noise models as well as with implementation on the 15-qubit ibmq_16_melbourne device. The quantum neuron and the QHAM are shown to be resilient to noise and require low qubit overhead and gate complexity. We benchmark the QHAM by testing its effective memory capacity and demonstrate its capabilities in the NISQ-era of quantum hardware. This demonstration of the first functional QHAM to be implemented in NISQ-era quantum hardware is a significant step in machine learning at the leading edge of quantum computing.Nathan Eli MillerSaibal MukhopadhyayNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nathan Eli Miller Saibal Mukhopadhyay A quantum Hopfield associative memory implemented on an actual quantum processor
description	Abstract In this work, we present a Quantum Hopfield Associative Memory (QHAM) and demonstrate its capabilities in simulation and hardware using IBM Quantum Experience.. The QHAM is based on a quantum neuron design which can be utilized for many different machine learning applications and can be implemented on real quantum hardware without requiring mid-circuit measurement or reset operations. We analyze the accuracy of the neuron and the full QHAM considering hardware errors via simulation with hardware noise models as well as with implementation on the 15-qubit ibmq_16_melbourne device. The quantum neuron and the QHAM are shown to be resilient to noise and require low qubit overhead and gate complexity. We benchmark the QHAM by testing its effective memory capacity and demonstrate its capabilities in the NISQ-era of quantum hardware. This demonstration of the first functional QHAM to be implemented in NISQ-era quantum hardware is a significant step in machine learning at the leading edge of quantum computing.
format	article
author	Nathan Eli Miller Saibal Mukhopadhyay
author_facet	Nathan Eli Miller Saibal Mukhopadhyay
author_sort	Nathan Eli Miller
title	A quantum Hopfield associative memory implemented on an actual quantum processor
title_short	A quantum Hopfield associative memory implemented on an actual quantum processor
title_full	A quantum Hopfield associative memory implemented on an actual quantum processor
title_fullStr	A quantum Hopfield associative memory implemented on an actual quantum processor
title_full_unstemmed	A quantum Hopfield associative memory implemented on an actual quantum processor
title_sort	quantum hopfield associative memory implemented on an actual quantum processor
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ccb7a068134b4afa8df8a3a851794bb7
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A quantum Hopfield associative memory implemented on an actual quantum processor

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