Resiliency analysis and improvement of variational quantum factoring in superconducting qubit

Ling Qiu; Mahabubul Alam; Abdullah Ash-Saki; Swaroop Ghosh

doi:10.1145/3370748.3406586

Resiliency analysis and improvement of variational quantum factoring in superconducting qubit

Ling Qiu, Mahabubul Alam, Abdullah Ash-Saki, Swaroop Ghosh

Electrical Engineering

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

2 Scopus citations

Abstract

Variational algorithm using Quantum Approximate Optimization Algorithm (QAOA) can solve the prime factorization problem in near-Term noisy quantum computers. Conventional Variational Quantum Factoring (VQF) requires a large number of 2-qubit gates (especially for factoring a large number) resulting in deep circuits. The output quality of the deep quantum circuit is degraded due to errors limiting the computational power of quantum computing. In this paper, we explore various transformations to optimize the QAOA circuit for integer factorization. We propose two criteria to select the optimal quantum circuit that can improve the noise resiliency of VQF.

Original language	English (US)
Title of host publication	Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020
Publisher	Association for Computing Machinery
ISBN (Electronic)	9781450370530
DOIs	https://doi.org/10.1145/3370748.3406586
State	Published - Aug 10 2020
Event	2020 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020 - Virtual, Online, United States Duration: Aug 10 2020 → Aug 12 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2020 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020
Country/Territory	United States
City	Virtual, Online
Period	8/10/20 → 8/12/20

All Science Journal Classification (ASJC) codes

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

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10.1145/3370748.3406586

Cite this

Qiu, L., Alam, M., Ash-Saki, A., & Ghosh, S. (2020). Resiliency analysis and improvement of variational quantum factoring in superconducting qubit. In Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020 Article 3406586 (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3370748.3406586

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title = "Resiliency analysis and improvement of variational quantum factoring in superconducting qubit",

abstract = "Variational algorithm using Quantum Approximate Optimization Algorithm (QAOA) can solve the prime factorization problem in near-Term noisy quantum computers. Conventional Variational Quantum Factoring (VQF) requires a large number of 2-qubit gates (especially for factoring a large number) resulting in deep circuits. The output quality of the deep quantum circuit is degraded due to errors limiting the computational power of quantum computing. In this paper, we explore various transformations to optimize the QAOA circuit for integer factorization. We propose two criteria to select the optimal quantum circuit that can improve the noise resiliency of VQF.",

author = "Ling Qiu and Mahabubul Alam and Abdullah Ash-Saki and Swaroop Ghosh",

note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 2020 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020 ; Conference date: 10-08-2020 Through 12-08-2020",

year = "2020",

month = aug,

day = "10",

doi = "10.1145/3370748.3406586",

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Qiu, L, Alam, M, Ash-Saki, A & Ghosh, S 2020, Resiliency analysis and improvement of variational quantum factoring in superconducting qubit. in Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020., 3406586, ACM International Conference Proceeding Series, Association for Computing Machinery, 2020 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020, Virtual, Online, United States, 8/10/20. https://doi.org/10.1145/3370748.3406586

Resiliency analysis and improvement of variational quantum factoring in superconducting qubit. / Qiu, Ling; Alam, Mahabubul; Ash-Saki, Abdullah et al.
Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED 2020. Association for Computing Machinery, 2020. 3406586 (ACM International Conference Proceeding Series).

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

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T1 - Resiliency analysis and improvement of variational quantum factoring in superconducting qubit

AU - Qiu, Ling

AU - Alam, Mahabubul

AU - Ash-Saki, Abdullah

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PY - 2020/8/10

Y1 - 2020/8/10

N2 - Variational algorithm using Quantum Approximate Optimization Algorithm (QAOA) can solve the prime factorization problem in near-Term noisy quantum computers. Conventional Variational Quantum Factoring (VQF) requires a large number of 2-qubit gates (especially for factoring a large number) resulting in deep circuits. The output quality of the deep quantum circuit is degraded due to errors limiting the computational power of quantum computing. In this paper, we explore various transformations to optimize the QAOA circuit for integer factorization. We propose two criteria to select the optimal quantum circuit that can improve the noise resiliency of VQF.

AB - Variational algorithm using Quantum Approximate Optimization Algorithm (QAOA) can solve the prime factorization problem in near-Term noisy quantum computers. Conventional Variational Quantum Factoring (VQF) requires a large number of 2-qubit gates (especially for factoring a large number) resulting in deep circuits. The output quality of the deep quantum circuit is degraded due to errors limiting the computational power of quantum computing. In this paper, we explore various transformations to optimize the QAOA circuit for integer factorization. We propose two criteria to select the optimal quantum circuit that can improve the noise resiliency of VQF.

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M3 - Conference contribution

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Resiliency analysis and improvement of variational quantum factoring in superconducting qubit

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