Building scalable variational circuit training for machine learning tasks

Kathleen E. Hamilton; Emily Lynn; Tyler Kharazi; Titus Morris; Ryan S. Bennink; Raphael C. Pooser

doi:10.1109/DAC18074.2021.9586171

Building scalable variational circuit training for machine learning tasks

Kathleen E. Hamilton, Emily Lynn, Tyler Kharazi, Titus Morris, Ryan S. Bennink, Raphael C. Pooser

Quantum Computational Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Parameterized quantum circuits (PQC) have emerged as a quantum analogue of deep neural networks and can be trained for discriminative or generative tasks and can be trained with gradient-based optimization on near-term quantum devices [1], [2], [3]. In the current era of quantum computing, known as the noisy intermediate scale quantum (NISQ) era [4], these devices contain a moderate number of qubits (< 100), and algorithmic performance is strongly impacted by hardware noise. Additionally, the training of PQCs are hybrid algorithms, in which the computational workflow is split between quantum and classical computing platforms.

Original language	English
Title of host publication	2021 58th ACM/IEEE Design Automation Conference, DAC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1351
Number of pages	1
ISBN (Electronic)	9781665432740
DOIs	https://doi.org/10.1109/DAC18074.2021.9586171
State	Published - Dec 5 2021
Event	58th ACM/IEEE Design Automation Conference, DAC 2021 - San Francisco, United States Duration: Dec 5 2021 → Dec 9 2021

Publication series

Name	Proceedings - Design Automation Conference
Volume	2021-December
ISSN (Print)	0738-100X

Conference

Conference	58th ACM/IEEE Design Automation Conference, DAC 2021
Country/Territory	United States
City	San Francisco
Period	12/5/21 → 12/9/21

Funding

This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC0500OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. Acknowledgements: This research used quantum computing resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. Work by DOE Office of Science User Facilities Division. This work was supported as part of the ASCR Testbed Pathfinder Program at Oak

Funders	Funder number
U.S. Department of Energy
Office of Science	DE-AC05-00OR22725
Advanced Scientific Computing Research

Keywords

NISQ computing
error mitigation
noise characterization
quantum computing

Access to Document

10.1109/DAC18074.2021.9586171

Cite this

Hamilton, K. E., Lynn, E., Kharazi, T., Morris, T., Bennink, R. S., & Pooser, R. C. (2021). Building scalable variational circuit training for machine learning tasks. In 2021 58th ACM/IEEE Design Automation Conference, DAC 2021 (pp. 1351). (Proceedings - Design Automation Conference; Vol. 2021-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DAC18074.2021.9586171

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abstract = "Parameterized quantum circuits (PQC) have emerged as a quantum analogue of deep neural networks and can be trained for discriminative or generative tasks and can be trained with gradient-based optimization on near-term quantum devices [1], [2], [3]. In the current era of quantum computing, known as the noisy intermediate scale quantum (NISQ) era [4], these devices contain a moderate number of qubits (< 100), and algorithmic performance is strongly impacted by hardware noise. Additionally, the training of PQCs are hybrid algorithms, in which the computational workflow is split between quantum and classical computing platforms.",

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doi = "10.1109/DAC18074.2021.9586171",

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Hamilton, KE, Lynn, E, Kharazi, T, Morris, T , Bennink, RS & Pooser, RC 2021, Building scalable variational circuit training for machine learning tasks. in 2021 58th ACM/IEEE Design Automation Conference, DAC 2021. Proceedings - Design Automation Conference, vol. 2021-December, Institute of Electrical and Electronics Engineers Inc., pp. 1351, 58th ACM/IEEE Design Automation Conference, DAC 2021, San Francisco, United States, 12/5/21. https://doi.org/10.1109/DAC18074.2021.9586171

Building scalable variational circuit training for machine learning tasks. / Hamilton, Kathleen E.; Lynn, Emily; Kharazi, Tyler et al.
2021 58th ACM/IEEE Design Automation Conference, DAC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1351 (Proceedings - Design Automation Conference; Vol. 2021-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Parameterized quantum circuits (PQC) have emerged as a quantum analogue of deep neural networks and can be trained for discriminative or generative tasks and can be trained with gradient-based optimization on near-term quantum devices [1], [2], [3]. In the current era of quantum computing, known as the noisy intermediate scale quantum (NISQ) era [4], these devices contain a moderate number of qubits (< 100), and algorithmic performance is strongly impacted by hardware noise. Additionally, the training of PQCs are hybrid algorithms, in which the computational workflow is split between quantum and classical computing platforms.

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Building scalable variational circuit training for machine learning tasks

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Keywords

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