Survey on Quantum Noise-aware Machine Learning

Chao Lu; Shamik Kundu; Ayush Arunachalam; Kanad Basu

doi:10.1109/DCAS53974.2022.9845619

Survey on Quantum Noise-aware Machine Learning

Chao Lu
, Shamik Kundu
, Ayush Arunachalam
, Kanad Basu

Advanced Computing for Life Sciences and

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

5 Scopus citations

Abstract

Quantum Computing demonstrates potential exponential speedup over classical computing in a plethora of tasks including chemistry simulation, linear algebra, and large integer factorization. Machine learning is one such popular application that benefits from this advantage of quantum computers to facilitate speedup. However, due to the inherent noise in quantum computers, machine learning algorithms encounter problems relating to fidelity and accuracy. Existing research has addressed these issues pertaining to the unreliable execution of machine learning models in noisy quantum computers. In this paper, we explore the effects of noise in quantum machine learning and demonstrate approaches to mitigate this issue.

Original language	English
Title of host publication	Proceedings of the 2022 IEEE Dallas Circuits and Systems Conference, DCAS 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665498852
DOIs	https://doi.org/10.1109/DCAS53974.2022.9845619
State	Published - 2022
Event	15th IEEE Dallas Circuits and Systems Conference, DCAS 2022 - Richardson, United States Duration: Jun 17 2022 → Jun 19 2022

Publication series

Name	Proceedings of the 2022 IEEE Dallas Circuits and Systems Conference, DCAS 2022

Conference

Conference	15th IEEE Dallas Circuits and Systems Conference, DCAS 2022
Country/Territory	United States
City	Richardson
Period	06/17/22 → 06/19/22

Keywords

Quantum Machine Learning
Quantum Noise

Access to Document

10.1109/DCAS53974.2022.9845619

Cite this

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title = "Survey on Quantum Noise-aware Machine Learning",

abstract = "Quantum Computing demonstrates potential exponential speedup over classical computing in a plethora of tasks including chemistry simulation, linear algebra, and large integer factorization. Machine learning is one such popular application that benefits from this advantage of quantum computers to facilitate speedup. However, due to the inherent noise in quantum computers, machine learning algorithms encounter problems relating to fidelity and accuracy. Existing research has addressed these issues pertaining to the unreliable execution of machine learning models in noisy quantum computers. In this paper, we explore the effects of noise in quantum machine learning and demonstrate approaches to mitigate this issue.",

keywords = "Quantum Machine Learning, Quantum Noise",

author = "Chao Lu and Shamik Kundu and Ayush Arunachalam and Kanad Basu",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 15th IEEE Dallas Circuits and Systems Conference, DCAS 2022 ; Conference date: 17-06-2022 Through 19-06-2022",

year = "2022",

doi = "10.1109/DCAS53974.2022.9845619",

language = "English",

series = "Proceedings of the 2022 IEEE Dallas Circuits and Systems Conference, DCAS 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of the 2022 IEEE Dallas Circuits and Systems Conference, DCAS 2022",

}

Lu, C, Kundu, S, Arunachalam, A & Basu, K 2022, Survey on Quantum Noise-aware Machine Learning. in Proceedings of the 2022 IEEE Dallas Circuits and Systems Conference, DCAS 2022. Proceedings of the 2022 IEEE Dallas Circuits and Systems Conference, DCAS 2022, Institute of Electrical and Electronics Engineers Inc., 15th IEEE Dallas Circuits and Systems Conference, DCAS 2022, Richardson, United States, 06/17/22. https://doi.org/10.1109/DCAS53974.2022.9845619

Survey on Quantum Noise-aware Machine Learning. / Lu, Chao; Kundu, Shamik; Arunachalam, Ayush et al.
Proceedings of the 2022 IEEE Dallas Circuits and Systems Conference, DCAS 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the 2022 IEEE Dallas Circuits and Systems Conference, DCAS 2022).

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

TY - GEN

T1 - Survey on Quantum Noise-aware Machine Learning

AU - Lu, Chao

AU - Kundu, Shamik

AU - Arunachalam, Ayush

AU - Basu, Kanad

PY - 2022

Y1 - 2022

N2 - Quantum Computing demonstrates potential exponential speedup over classical computing in a plethora of tasks including chemistry simulation, linear algebra, and large integer factorization. Machine learning is one such popular application that benefits from this advantage of quantum computers to facilitate speedup. However, due to the inherent noise in quantum computers, machine learning algorithms encounter problems relating to fidelity and accuracy. Existing research has addressed these issues pertaining to the unreliable execution of machine learning models in noisy quantum computers. In this paper, we explore the effects of noise in quantum machine learning and demonstrate approaches to mitigate this issue.

AB - Quantum Computing demonstrates potential exponential speedup over classical computing in a plethora of tasks including chemistry simulation, linear algebra, and large integer factorization. Machine learning is one such popular application that benefits from this advantage of quantum computers to facilitate speedup. However, due to the inherent noise in quantum computers, machine learning algorithms encounter problems relating to fidelity and accuracy. Existing research has addressed these issues pertaining to the unreliable execution of machine learning models in noisy quantum computers. In this paper, we explore the effects of noise in quantum machine learning and demonstrate approaches to mitigate this issue.

KW - Quantum Machine Learning

KW - Quantum Noise

UR - https://www.scopus.com/pages/publications/85137676004

U2 - 10.1109/DCAS53974.2022.9845619

DO - 10.1109/DCAS53974.2022.9845619

M3 - Conference contribution

AN - SCOPUS:85137676004

T3 - Proceedings of the 2022 IEEE Dallas Circuits and Systems Conference, DCAS 2022

BT - Proceedings of the 2022 IEEE Dallas Circuits and Systems Conference, DCAS 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th IEEE Dallas Circuits and Systems Conference, DCAS 2022

Y2 - 17 June 2022 through 19 June 2022

ER -

Survey on Quantum Noise-aware Machine Learning

Abstract

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Conference

Keywords

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