qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation

Stan Van Der Linde, Willem De Kok, Tariq Bontekoe*, Sebastian Feld

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

Compiling a quantum circuit for specific quantum hardware is a challenging task. Moreover, current quantum computers have severe hardware limitations. To make the most use of the limited resources, the compilation process should be optimized. To improve currents methods, Reinforcement Learning (RL), a technique in which an agent interacts with an environment to learn complex policies to attain a specific goal, can be used. In this work, we present qgym, a software framework derived from the OpenAI gym, together with environments that are specifically tailored towards quantum compilation. The goal of qgym is to connect the research fields of Artificial Intelligence (AI) with quantum compilation by abstracting parts of the process that are irrelevant to either domain. It can be used to train and benchmark RL agents and algorithms in highly customizable environments.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023
EditorsHausi Muller, Yuri Alexev, Andrea Delgado, Greg Byrd
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages26-30
Number of pages5
ISBN (Electronic)9798350343236
DOIs
Publication statusPublished - 2023
Event4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023 - Bellevue, United States
Duration: 17 Sept 202322 Sept 2023

Publication series

NameProceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023
Volume2

Conference

Conference4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023
Country/TerritoryUnited States
CityBellevue
Period17/09/2322/09/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Quantum Compilation
  • Qubit Mapping
  • Qubit Routing
  • Reinforcement Learning
  • Scheduling

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