Accelerating Quantum Approximate Optimization Algorithm using Machine Learning

Mahabubul Alam, Abdullah Ash-Saki, Swaroop Ghosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a machine learning based approach to accelerate quantum approximate optimization algorithm (QAOA) implementation which is a promising quantum-classical hybrid algorithm to prove the so-called quantum supremacy. In QAOA, a parametric quantum circuit and a classical optimizer iterates in a closed loop to solve hard combinatorial optimization problems. The performance of QAOA improves with increasing number of stages (depth) in the quantum circuit. However, two new parameters are introduced with each added stage for the classical optimizer increasing the number of optimization loop iterations. We note a correlation among parameters of the lower-depth and the higher-depth QAOA implementations and, exploit it by developing a machine learning model to predict the gate parameters close to the optimal values. As a result, the optimization loop converges in a fewer number of iterations. We choose graph MaxCut problem as a prototype to solve using QAOA. We perform a feature extraction routine using 100 different QAOA instances and develop a training data-set with 13, 860 optimal parameters. We present our analysis for 4 flavors of regression models and 4 flavors of classical optimizers. Finally, we show that the proposed approach can curtail the number of optimization iterations by on average 44.9% (up to 65.7%) from an analysis performed with 264 flavors of graphs.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020
EditorsGiorgio Di Natale, Cristiana Bolchini, Elena-Ioana Vatajelu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages686-689
Number of pages4
ISBN (Electronic)9783981926347
DOIs
StatePublished - Mar 2020
Event2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020 - Grenoble, France
Duration: Mar 9 2020Mar 13 2020

Publication series

NameProceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020

Conference

Conference2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020
CountryFrance
CityGrenoble
Period3/9/203/13/20

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality
  • Modeling and Simulation
  • Electrical and Electronic Engineering

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  • Cite this

    Alam, M., Ash-Saki, A., & Ghosh, S. (2020). Accelerating Quantum Approximate Optimization Algorithm using Machine Learning. In G. Di Natale, C. Bolchini, & E-I. Vatajelu (Eds.), Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020 (pp. 686-689). [9116348] (Proceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/DATE48585.2020.9116348