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Quantum adiabatic machine learning by zooming into a region of the energy surface

Zlokapa, Alexander and Mott, Alex and Job, Joshua and Vlimant, Jean-Roch and Lidar, Daniel and Spiropulu, Maria (2020) Quantum adiabatic machine learning by zooming into a region of the energy surface. Physical Review A, 102 (6). Art. No. 062405. ISSN 2469-9926. https://resolver.caltech.edu/CaltechAUTHORS:20200218-124201888

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Abstract

Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. We propose QAML-Z, an algorithm that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAML-Z matches classical deep neural network performance at small training set sizes and reduces the performance margin between QAML and classical deep neural networks by almost 50% at large training set sizes, as measured by area under the receiver operating characteristic curve. The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1103/PhysRevA.102.062405DOIArticle
https://arxiv.org/abs/1908.04480arXivDiscussion Paper
ORCID:
AuthorORCID
Zlokapa, Alexander0000-0002-4153-8646
Mott, Alex0000-0003-1050-6622
Vlimant, Jean-Roch0000-0002-9705-101X
Spiropulu, Maria0000-0001-8172-7081
Alternate Title:Quantum adiabatic machine learning with zooming
Additional Information:© 2020 American Physical Society. Received 6 July 2020; accepted 9 November 2020; published 4 December 2020. Part of this work was conducted at “iBanks,” the AI GPU cluster at Caltech. We acknowledge NVIDIA, SuperMicro, and the Kavli Foundation for their support of “iBanks.” This work is partially supported by a DOE/HEP QuantISED program grant, Quantum Machine Learning and Quantum Computation Frameworks (QMLQCF) for HEP, Grant No. DE-SC0019227. The work is also supported in part by the AT&T Foundry Innovation Centers through INQNET, a program for accelerating quantum technologies. The research is based upon work (partially) supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) and the Defense Advanced Research Projects Agency (DARPA), via the US Army Research Office Contract No. W911NF-17-C-0050. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the US Government. The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.
Group:INQNET
Funders:
Funding AgencyGrant Number
NVIDIA CorporationUNSPECIFIED
SuperMicro CorporationUNSPECIFIED
Kavli FoundationUNSPECIFIED
Department of Energy (DOE)DE-SC0019227
INQNETUNSPECIFIED
Office of the Director of National Intelligence (ODNI)UNSPECIFIED
Intelligence Advanced Research Projects Activity (IARPA)UNSPECIFIED
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Army Research Office (ARO)W911NF-17-C-0050
Issue or Number:6
Record Number:CaltechAUTHORS:20200218-124201888
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200218-124201888
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101325
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:18 Feb 2020 21:34
Last Modified:07 Dec 2020 18:38

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