Ising selector machine by Kerr parametric oscillators

TL;DR

This paper demonstrates that a network of Kerr parametric oscillators can be used as an Ising selector machine, capable of targeting various energy states, including excited states, by tuning system parameters.

Contribution

It introduces a method to steer Kerr parametric oscillators to access specific Ising energy states, expanding the capabilities of Ising machines beyond ground state optimization.

Findings

Numerical simulations show noise preserves energy landscape structure.

Tuning pump-cavity detuning controls the energy state targeted.

Targeted states are exponentially more probable than others.

Abstract

Ising machines are physical platforms designed to minimize the energy of classical Ising Hamiltonians, yet accessing specific excited states remains an open challenge of both fundamental and practical relevance. In this letter we show that a network of Kerr parametric oscillators (KPOs) naturally implements an Ising selector machine. By tuning the frequency detuning between the parametric pump and the oscillator resonances, the system can be steered to converge close to the ground state, the highest-energy configuration, or targeted intermediate excited states. Beyond mean field, numerical simulations based on the truncated Wigner approximation demonstrate that noise insertion preserves the energetic structure of the landscape. The targeted state emerges with an exponentially enhanced probability over the rest of the Ising spectrum. Our results establish the pump-cavity detuning as a control knob for navigating the full Ising energy landscape, opening a route to applications in Boltzmann sampling, hardness characterization, and spectral analysis of combinatorial problems.