MAQ: Machine Learning on Adiabatic Quantum Computers

Prasanna Date; Kathleen Hamilton; Robert Patton; Travis Humble; Thomas Potok

doi:10.11578/dc.20240307.2

MAQ: Machine Learning on Adiabatic Quantum Computers

Research output: Non-textual form › Software

Abstract

Machine Learning on Adiabatic Quantum Computers (MAQ) is a library of algorithms used to train machine learning models on adiabatic quantum computers.

Original language	American English
Place of Publication	United States
DOIs	https://doi.org/10.11578/dc.20240307.2
State	Published - 2024

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10.11578/dc.20240307.2

Cite this

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title = "MAQ: Machine Learning on Adiabatic Quantum Computers",

abstract = "Machine Learning on Adiabatic Quantum Computers (MAQ) is a library of algorithms used to train machine learning models on adiabatic quantum computers.",

author = "Prasanna Date and Kathleen Hamilton and Robert Patton and Travis Humble and Thomas Potok",

year = "2024",

doi = "10.11578/dc.20240307.2",

language = "American English",

}

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AU - Date, Prasanna

AU - Hamilton, Kathleen

AU - Patton, Robert

AU - Humble, Travis

AU - Potok, Thomas

PY - 2024

Y1 - 2024

N2 - Machine Learning on Adiabatic Quantum Computers (MAQ) is a library of algorithms used to train machine learning models on adiabatic quantum computers.

AB - Machine Learning on Adiabatic Quantum Computers (MAQ) is a library of algorithms used to train machine learning models on adiabatic quantum computers.

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DO - 10.11578/dc.20240307.2

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CY - United States

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