Hauptnavigation

Franken/etal/2020a: Gradient-free quantum optimization on NISQ devices

Bibtype Article
Bibkey Franken/etal/2020a
Author Franken, Lukas and Georgiev, Bogdan and Mücke, Sascha and Wolter, Moritz and Piatkowski, Nico and Bauckhage, Christian
Ls8autor Mücke, Sascha
Piatkowski, Nico
Title Gradient-free quantum optimization on NISQ devices
Abstract Variational Quantum Eigensolvers (VQEs) have recently attracted considerable attention. Yet, in practice, they still suffer from the efforts for estimating cost function gradients for large parameter sets or resource-demanding reinforcement strategies. Here, we therefore consider recent advances in weight-agnostic learning and propose a strategy that addresses the trade-off between finding appropriate circuit architectures and parameter tuning. We investigate the use of NEAT-inspired algorithms which evaluate circuits via genetic competition and thus circumvent issues due to exceeding numbers of parameters. Our methods are tested both via simulation and on real quantum hardware and are used to solve the transverse Ising Hamiltonian and the Sherrington-Kirkpatrick spin model.
Month 12
Year 2020
Projekt ML2R
Url https://arxiv.org/pdf/2012.13453



  • Privacy Policy
  • Imprint