We’ve built an optimization machine that can find specific 'sub-optimal' solutions, which is often more useful than finding the 'best' one.
April 17, 2026
Original Paper
Ising selector machine by Kerr parametric oscillators
arXiv · 2604.12718
The Takeaway
Standard Ising machines only look for the ground state (the global minimum). However, many real-world problems like diverse sampling require finding specific intermediate 'excited' states. By using Kerr parametric oscillators, this machine can be steered to converge on any specific energy level of the Hamiltonian. This unlocks the ability to solve complex combinatorial problems where solution diversity is more important than a single answer. It’s a massive upgrade for practitioners working on complex sampling or multi-modal optimization.
From the 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 hig