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  • Letter
  • Open Access

Deterministic and stochastic sampling of two coupled Kerr parametric oscillators

Gabriel Margiani1, Javier del Pino2, Toni L. Heugel2, Nicholas E. Bousse3, Sebastián Guerrero1, Thomas W. Kenny4, Oded Zilberberg5, Deividas Sabonis1, and Alexander Eichler1

  • 1Laboratory for Solid State Physics, ETH Zürich, CH-8093 Zürich, Switzerland
  • 2Institute for Theoretical Physics, ETH Zürich, CH-8093 Zürich, Switzerland
  • 3Departments of Mechanical Engineering, Stanford University, Stanford, California 94305, USA
  • 4Departments of Mechanical and Electrical Engineering, Stanford University, Stanford, California 94305, USA
  • 5Department of Physics, University of Konstanz, D-78457 Konstanz, Germany

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Phys. Rev. Research 5, L012029 – Published 27 February, 2023

DOI: https://doi.org/10.1103/PhysRevResearch.5.L012029

Abstract

The vision of building computational hardware for problem optimization has spurred large efforts in the physics community. In particular, networks of Kerr parametric oscillators (KPOs) are envisioned as simulators for finding the ground states of Ising Hamiltonians. It was shown, however, that KPO networks can feature large numbers of unexpected solutions that are difficult to sample with the existing deterministic (i.e., adiabatic) protocols. In this work, we experimentally realize a system of two classical coupled KPOs, and we find good agreement with the predicted mapping to Ising states. We then introduce a protocol based on stochastic sampling of the system, and we show how the resulting probability distribution can be used to identify the ground state of the corresponding Ising Hamiltonian. This method is akin to a Monte Carlo sampling of multiple out-of-equilibrium stationary states and is less prone to become trapped in local minima than deterministic protocols.

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Phys. Rev. Research 5, L012029– Published 27 February, 2023
  • Received 24 October 2022
  • Accepted 14 February 2023
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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