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06 September 2022
Conference Paper
Titel

Quantum Circuit Evolution on NISQ Devices

Abstract
Variational quantum circuits build the foundation for various classes of quantum algorithms. In a nutshell, the weights of a parametrized quantum circuit are varied until the empirical sampling distribution of the circuit is sufficiently close to a desired outcome. Numerical first-order methods are applied frequently to fit the parameters of the circuit, but most of the time, the circuit itself, that is, the actual composition of gates, is fixed. Methods for optimizing the circuit design jointly with the weights have been proposed, but empirical results are rather scarce. Here, we consider a simple evolutionary strategy that addresses the trade-off between finding appropriate circuit architectures and parameter tuning. We evaluate our method both via simulation and on actual quantum hardware. Our benchmark problems include the transverse field Ising Hamiltonian and the Sherrington-Kirkpatrick spin model. Despite the shortcomings of current noisy intermediate-scale quantum hardware, we find only a minor slowdown on actual quantum machines compared to simulations. Moreover, we investigate which mutation operations most significantly contribute to the optimization. The results provide intuition on how randomized search heuristics behave on actual quantum hardware and lay out a path for further refinement of evolutionary quantum gate circuits.
Author(s)
Franken, Lukas
Georgiev, Bogdan
Mücke, Sascha
TU Dortmund
Wolter, Moritz
University of Bonn
Heese, Raoul
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM
Bauckhage, Christian
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Piatkowski, Nico
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Hauptwerk
IEEE Congress on Evolutionary Computation, CEC 2022. Conference Proceedings
Project(s)
ML2R
Funder
Bundesministerium für Bildung und Forschung -BMBF-
Konferenz
Congress on Evolutionary Computation 2022
World Congress on Computational Intelligence 2022
Thumbnail Image
DOI
10.1109/CEC55065.2022.9870269
Language
English
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Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM
Tags
  • variational quantum c...

  • structure learning

  • evolutionary computat...

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