Learning Based Hardware-Centric Quantum Circuit Generation

Merel A. Schalkers*, Matthias Möller

*Corresponding author for this work

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

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In this paper we present an approach to find quantum circuits suitable to mimic probabilistic and search operations on a physical NISQ device. We present both a gradient based and a non-gradient based machine learning approach to optimize the created quantum circuits. In our optimization procedure we make use of a cost function that differentiates between the vector representing the probabilities of measurement of each basis state after applying our learned circuit and the desired probability vector. As such our quantum circuit generation (QCG) approach leads to thinner quantum circuits which behave better when executed on physical quantum computers. Our approach moreover ensures that the created quantum circuit obeys the restrictions of the chosen hardware. By catering to specific quantum hardware we can avoid unforeseen and potentially unnecessary circuit depth, and we return circuits that need no further transpilation. We present the results of running the created circuits on quantum computers by IBM, Rigetti and Quantum Inspire.

Original languageEnglish
Title of host publicationInnovations for Community Services - 22nd International Conference, I4CS 2022, Proceedings
EditorsFrank Phillipson, Gerald Eichler, Christian Erfurth, Günter Fahrnberger
Place of PublicationCham
Number of pages15
ISBN (Electronic)978-3-031-06668-9
ISBN (Print)978-3-031-06667-2
Publication statusPublished - 2022
Event22nd International Conference on Innovations for Community Services, I4CS 2022 - Delft, Netherlands
Duration: 13 Jun 202215 Jun 2022

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference22nd International Conference on Innovations for Community Services, I4CS 2022

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.


  • Hybrid quantum computing
  • NISQ
  • Quantum compiling
  • Quantum computing
  • Quantum machine learning


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