• Skip to main content
  • Skip to primary sidebar
AAAI

AAAI

Association for the Advancement of Artificial Intelligence

    • AAAI

      AAAI

      Association for the Advancement of Artificial Intelligence

  • About AAAIAbout AAAI
    • AAAI Officers and Committees
    • AAAI Staff
    • Bylaws of AAAI
    • AAAI Awards
      • Fellows Program
      • Classic Paper Award
      • Dissertation Award
      • Distinguished Service Award
      • Allen Newell Award
      • Outstanding Paper Award
      • Award for Artificial Intelligence for the Benefit of Humanity
      • Feigenbaum Prize
      • Patrick Henry Winston Outstanding Educator Award
      • Engelmore Award
      • AAAI ISEF Awards
      • Senior Member Status
      • Conference Awards
    • AAAI Resources
    • AAAI Mailing Lists
    • Past AAAI Presidential Addresses
    • Presidential Panel on Long-Term AI Futures
    • Past AAAI Policy Reports
      • A Report to ARPA on Twenty-First Century Intelligent Systems
      • The Role of Intelligent Systems in the National Information Infrastructure
    • AAAI Logos
    • News
  • aaai-icon_ethics-diversity-line-yellowEthics & Diversity
  • Conference talk bubbleConferences & Symposia
    • AAAI Conference
    • AIES AAAI/ACM
    • AIIDE
    • IAAI
    • ICWSM
    • HCOMP
    • Spring Symposia
    • Summer Symposia
    • Fall Symposia
    • Code of Conduct for Conferences and Events
  • PublicationsPublications
    • AAAI Press
    • AI Magazine
    • Conference Proceedings
    • AAAI Publication Policies & Guidelines
    • Request to Reproduce Copyrighted Materials
  • aaai-icon_ai-magazine-line-yellowAI Magazine
    • Issues and Articles
    • Author Guidelines
    • Editorial Focus
  • MembershipMembership
    • Member Login
    • Developing Country List
    • AAAI Chapter Program

  • Career CenterCareer Center
  • aaai-icon_ai-topics-line-yellowAITopics
  • aaai-icon_contact-line-yellowContact

Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems

February 1, 2023

Download PDF

Authors

Sami Khairy

Illinois Institute of Technology


Ruslan Shaydulin

Clemson University


Lukasz Cincio

Los Alamos National Laboratory


Yuri Alexeev

Argonne National Laboratory


Prasanna Balaprakash

Argonne National Laboratory


DOI:

10.1609/aaai.v34i03.5616


Abstract:

Quantum computing is a computational paradigm with the potential to outperform classical methods for a variety of problems. Proposed recently, the Quantum Approximate Optimization Algorithm (QAOA) is considered as one of the leading candidates for demonstrating quantum advantage in the near term. QAOA is a variational hybrid quantum-classical algorithm for approximately solving combinatorial optimization problems. The quality of the solution obtained by QAOA for a given problem instance depends on the performance of the classical optimizer used to optimize the variational parameters. In this paper, we formulate the problem of finding optimal QAOA parameters as a learning task in which the knowledge gained from solving training instances can be leveraged to find high-quality solutions for unseen test instances. To this end, we develop two machine-learning-based approaches. Our first approach adopts a reinforcement learning (RL) framework to learn a policy network to optimize QAOA circuits. Our second approach adopts a kernel density estimation (KDE) technique to learn a generative model of optimal QAOA parameters. In both approaches, the training procedure is performed on small-sized problem instances that can be simulated on a classical computer; yet the learned RL policy and the generative model can be used to efficiently solve larger problems. Extensive simulations using the IBM Qiskit Aer quantum circuit simulator demonstrate that our proposed RL- and KDE-based approaches reduce the optimality gap by factors up to 30.15 when compared with other commonly used off-the-shelf optimizers.

Topics: AAAI

Primary Sidebar

HOW TO CITE:

Sami Khairy||Ruslan Shaydulin||Lukasz Cincio||Yuri Alexeev||Prasanna Balaprakash Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems Proceedings of the AAAI Conference on Artificial Intelligence (2020) 2367-2375.

Sami Khairy||Ruslan Shaydulin||Lukasz Cincio||Yuri Alexeev||Prasanna Balaprakash Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems AAAI 2020, 2367-2375.

Sami Khairy||Ruslan Shaydulin||Lukasz Cincio||Yuri Alexeev||Prasanna Balaprakash (2020). Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems. Proceedings of the AAAI Conference on Artificial Intelligence, 2367-2375.

Sami Khairy||Ruslan Shaydulin||Lukasz Cincio||Yuri Alexeev||Prasanna Balaprakash. Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.2367-2375.

Sami Khairy||Ruslan Shaydulin||Lukasz Cincio||Yuri Alexeev||Prasanna Balaprakash. 2020. Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems. "Proceedings of the AAAI Conference on Artificial Intelligence". 2367-2375.

Sami Khairy||Ruslan Shaydulin||Lukasz Cincio||Yuri Alexeev||Prasanna Balaprakash. (2020) "Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems", Proceedings of the AAAI Conference on Artificial Intelligence, p.2367-2375

Sami Khairy||Ruslan Shaydulin||Lukasz Cincio||Yuri Alexeev||Prasanna Balaprakash, "Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems", AAAI, p.2367-2375, 2020.

Sami Khairy||Ruslan Shaydulin||Lukasz Cincio||Yuri Alexeev||Prasanna Balaprakash. "Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.2367-2375.

Sami Khairy||Ruslan Shaydulin||Lukasz Cincio||Yuri Alexeev||Prasanna Balaprakash. "Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 2367-2375.

Sami Khairy||Ruslan Shaydulin||Lukasz Cincio||Yuri Alexeev||Prasanna Balaprakash. Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems. AAAI[Internet]. 2020[cited 2023]; 2367-2375.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept All”, you consent to the use of ALL the cookies. However, you may visit "Cookie Settings" to provide a controlled consent.
Cookie SettingsAccept All
Manage consent

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
CookieDurationDescription
cookielawinfo-checkbox-analytics11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional11 monthsThe cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy11 monthsThe cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
Functional
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Analytics
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
Advertisement
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
Others
Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.
SAVE & ACCEPT