skip to main content
10.1145/3649329.3663523acmconferencesArticle/Chapter ViewAbstractPublication PagesdacConference Proceedingsconference-collections
research-article
Open access

Invited: Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm

Published: 07 November 2024 Publication History

Abstract

In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that have long challenged classical computational methods. Among these, the Quantum Approximate Optimization Algorithm (QAOA) stands out for its potential to efficiently solve the Max-Cut problem, a quintessential example of combinatorial optimization. However, practical application faces challenges due to current limitations on quantum computational resource. Our work optimizes QAOA initialization, using Graph Neural Networks (GNN) as a warm-start technique. This sacrifices affordable computational resource on classical computer to reduce quantum computational resource overhead, enhancing QAOA's effectiveness. Experiments with various GNN architectures demonstrate the adaptability and stability of our framework, highlighting the synergy between quantum algorithms and machine learning. Our findings show GNN's potential in improving QAOA performance, opening new avenues for hybrid quantum-classical approaches in quantum computing and contributing to practical applications.

References

[1]
Jinglei Cheng, Haoqing Deng, and Xuehai Qian. 2020. Accqoc: accelerating quantum optimal control based pulse generation. In 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA). IEEE. IEEE, Valencia, Spain, 543--555.
[2]
Daniel J Egger, Jakub Mareček, and Stefan Woerner. 2021. Warm-starting quantum optimization. Quantum, 5, 479.
[3]
Gian Giacomo Guerreschi and Anne Y. Matsuura. 2019. Qaoa for max-cut requires hundreds of qubits for quantum speed-up. Scientific reports, 9, 1, 1--7.
[4]
Nishant Jain, Brian Coyle, Elham Kashefi, and Niraj Kumar. 2022. Graph neural network initialisation of quantum approximate optimisation. Quantum, 6, 861.
[5]
Gang Liu, Tong Zhao, Eric Inae, Tengfei Luo, and Meng Jiang. 2023. Semi-supervised graph imbalanced regression. arXiv preprint arXiv:2305.12087.
[6]
Danylo Lykov, Ruslan Shaydulin, Yue Sun, Yuri Alexeev, and Marco Pistoia. 2023. Fast simulation of high-depth qaoa circuits. In Proceedings of the SC'23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, 1443--1451.
[7]
John Preskill. 2018. Quantum computing in the NISQ era and beyond. Quantum, 2, 79. arxiv1801.00862.
[8]
Reuben Tate, Majid Farhadi, Creston Herold, Greg Mohler, and Swati Gupta. 2023. Bridging classical and quantum with sdp initialized warm-starts for qaoa. ACM Transactions on Quantum Computing, 4, 2, 1--39.
[9]
Jonathan Wurtz and Danylo Lykov. 2021. Fixed-angle conjectures for the quantum approximate optimization algorithm on regular maxcut graphs. Physical Review A, 104, 5, 052419.

Cited By

View all
  • (2024)Quantum Artificial Intelligence: A Brief SurveyKI - Künstliche Intelligenz10.1007/s13218-024-00871-8Online publication date: 4-Nov-2024

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
DAC '24: Proceedings of the 61st ACM/IEEE Design Automation Conference
June 2024
2159 pages
ISBN:9798400706011
DOI:10.1145/3649329
This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2024

Check for updates

Author Tags

  1. QAOA
  2. GNN
  3. max-cut
  4. quantum computing

Qualifiers

  • Research-article

Conference

DAC '24
Sponsor:
DAC '24: 61st ACM/IEEE Design Automation Conference
June 23 - 27, 2024
CA, San Francisco, USA

Acceptance Rates

Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

Upcoming Conference

DAC '25
62nd ACM/IEEE Design Automation Conference
June 22 - 26, 2025
San Francisco , CA , USA

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)169
  • Downloads (Last 6 weeks)62
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Quantum Artificial Intelligence: A Brief SurveyKI - Künstliche Intelligenz10.1007/s13218-024-00871-8Online publication date: 4-Nov-2024

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media