QUBO formulations for training machine learning models

Training machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum...

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Bibliographic Details
Published in:Scientific reports Vol. 11; no. 1; pp. 10029 - 10
Main Authors: Date, Prasanna, Arthur, Davis, Pusey-Nazzaro, Lauren
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 11.05.2021
Nature Publishing Group
Nature Portfolio
Subjects:
639/705/117
639/766/259
Adiabatic
Computer applications
Computer science
Computers
Humanities and Social Sciences
Information theory and computation
Learning algorithms
Machine learning
MATHEMATICS AND COMPUTING
multidisciplinary
Quantum computing
Regression analysis
Science
Science (multidisciplinary)
Support vector machines
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Training machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.
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USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
AC05-00OR22725
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-021-89461-4