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Communications Physics

Discretized spin interactions enhance higher-order analog Ising machines

Robbe De Prins, Guy Van der Sande, Peter Bienstman, Thomas Van Vaerenbergh

Published Aug 5, 2026• 0 citations
Publisher

Abstract

Ising machines are specialized devices designed to efficiently solve combinatorial optimization problems. Among such problems, Boolean Satisfiability (SAT) is particularly relevant in industrial applications. To solve SAT problems using Ising machines, it is crucial to incorporate higher-order interactions. However, in analog Ising machines, interactions of different orders scale unevenly with the continuous spin amplitudes, introducing imbalances that can significantly degrade performance. Here we present a numerical comparison of methods to mitigate these imbalances, evaluating time-to-solution and success rate on uniform random 3-SAT and 4-SAT instances. Our results show that the most effective approach employs spin interactions that are proportional to the signs of spins, rather than their continuous amplitudes. We further demonstrate that smooth approximations of this method make it compatible with analog hardware. These findings underscore the central role of spin-sign-based interactions in enabling robust and scalable analog Ising machine dynamics. Ising machines (IMs) offer promising solutions for combinatorial optimization problems yet face challenges with imbalances in higher-order interactions when applied to Boolean Satisfiability (SAT) tasks. Here, the authors demonstrate that employing spin-sign-based interactions effectively mitigates these imbalances, enhancing performance and compatibility with analog hardware, thus advancing robust and scalable IM dynamics.

DOI
10.1038/s42005-026-02781-7

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Ising machines are specialized devices designed to efficiently solve combinatorial optimization problems. Among such problems, Boolean Satisfiability (SAT) is particularly relevant in industrial applications. To solve SAT problems using Ising machines, it is crucial to incorporate higher-order interactions. However, in analog Ising machines, interactions of different orders scale unevenly with the continuous spin amplitudes, introducing imbalances that can significantly degrade performance. Here we present a numerical comparison of methods to mitigate these imbalances, evaluating time-to-solution and success rate on uniform random 3-SAT and 4-SAT instances. Our results show that the most effective approach employs spin interactions that are proportional to the signs of spins, rather than their continuous amplitudes. We further demonstrate that smooth approximations of this method make it compatible with analog hardware. These findings underscore the central role of spin-sign-based interactions in enabling robust and scalable analog Ising machine dynamics. Ising machines (IMs) offer promising solutions for combinatorial optimization problems yet face challenges with imbalances in higher-order interactions when applied to Boolean Satisfiability (SAT) tasks. Here, the authors demonstrate that employing spin-sign-based interactions effectively mitigates these imbalances, enhancing performance and compatibility with analog hardware, thus advancing robust and scalable IM dynamics.
Ising machines are specialized devices designed to efficiently solve combinatorial optimization problems. They consist of artificial spins that evolve towards a low-energy configuration representing a problem’s solution. Most realistic problems require both spin-spin couplings and external fields. In Ising machines with analog spins, these interactions scale differently with the continuous spin amplitudes, leading to imbalances that affect performance. Various techniques have been proposed to mitigate this issue, but their performance has not been benchmarked. We address this gap through a numerical analysis. We evaluate the time-to-solution of these methods across three distinct problem classes with up to 2400 spins. Our results show that the most effective way to incorporate external fields is through an approach where the spin interactions are proportional to the spin signs, rather than their continuous amplitudes. Solving real-world optimization problems on analog Ising machines requires the use of external fields that bias individual spins. This work shows that the best performance is achieved using spin interactions proportional to the spin signs rather than their analog amplitudes.
Many combinatorial optimization problems (COPs) are naturally expressed using variables that take on more than two discrete values. To solve such problems using Ising machines (IMs)—specialized analog or digital devices designed to solve COPs efficiently—these multivalued integers must be encoded using binary spin variables. A common approach is one-hot encoding, where each variable is represented by a group of spins constrained so that exactly one spin is in the “up” state. However, this encoding introduces energy barriers: changing an integer’s value requires flipping two spins and passing through an invalid intermediate state. This creates rugged energy landscapes that may hinder optimization. We propose a higher-order Ising formulation for max-3-cut, a canonical COP involving integers with three possible states that provides a minimal setting for assessing multivalued formulations on IMs. Our formulation preserves valid configurations under single-spin updates. The resulting energy landscapes are smoother, and we show that this remains true even when the binary variables are relaxed to continuous values, making it well suited for analog IMs as well. Benchmarking on such an IM, we find that the higher-order formulation leads to higher success rates and faster time-to-solution than when one-hot encoding is employed.
With the slowdown of improvement in conventional von Neumann systems, increasing attention is paid to novel paradigms such as Ising machines. They have very different approach to solving combinatorial optimization problems. Ising machines have shown great potential in solving binary optimization problems like MaxCut. In this paper, we present an analysis of these systems in boolean satisfiability (SAT) problems. We demonstrate that, in the case of 3-SAT, a basic architecture fails to produce meaningful acceleration, largely due to the relentless progress made in conventional SAT solvers. Nevertheless, careful analysis attributes part of the failure to the lack of two important components: cubic interactions and efficient randomization heuristics. To overcome these limitations, we add proper architectural support for cubic interaction on a state-of-the-art Ising machine. More importantly, we propose a novel semantic-aware annealing schedule that makes the search-space navigation much more efficient than existing annealing heuristics. Using numerical simulations, we show that such an "Augmented" Ising Machine for SAT is projected to outperform state-of-the-art software-based, GPU-based and conventional hardware SAT solvers by orders of magnitude.
The Ising model provides a natural mapping for many computationally hard combinatorial optimization problems (COPs). Consequently, dynamical system-inspired computing models and hardware platforms that minimize the Ising Hamiltonian, have recently been proposed as a potential candidate for solving COPs, with the promise of significant performance benefit. However, the Ising model, and consequently, the corresponding dynamical system-based computational models primarily consider quadratic interactions among the nodes. Computational models considering higher order interactions among Ising spins remain largely unexplored. Therefore, in this work, we propose dynamical-system-based computational models to consider higher order (>2) interactions among the Ising spins, which subsequently, enables us to propose computational models to directly solve many COPs that entail such higher order interactions (COPs on hypergraphs). Specifically, we demonstrate our approach by developing dynamical systems to compute the solution for the Boolean NAE-K-SAT (K is greater than 3) problem as well as solve the Max-K-Cut of a hypergraph. Our work advances the potential of the physics-inspired 'toolbox' for solving COPs.
We introduce and explore a Kerr soliton Ising machine with all-to-all connectivity and up to tripartite (cubic) interactions, unlocking efficient approaches for combinatorial optimization. The machine is an ensemble of solitons in a Kerr resonator, where a programmable optoelectronic feedback circuit generates amplitude mixing terms involving up to three arbitrary soliton spins in the system Hamiltonian. This capability offers a key advantage for solving canonical NP-complete problems like Boolean satisfiability (SAT) with three literal clauses, substantially reducing the spin overhead compared to quadratic-only Ising mappings. To explore our cubic Ising machine, we solve randomly generated 3-SAT instances of 250 variables and 1000 clauses, approaching the intrinsic satisfiability threshold. Our results highlight Kerr solitons as a flexible and energy-efficient analog computing substrate, with the potential to address challenges in combinatorial optimization, artificial intelligence, and machine learning.
Abstract Ising Machines are emerging hardware architectures that efficiently solve NP-hard combinatorial optimization problems. Generally, combinatorial problems are transformed into quadratic unconstrained binary optimization (QUBO) form, but this transformation often complicates the solution landscape, degrading performance, especially for multi-state problems. To address this challenge, we model spin interactions as generalized boolean logic function to significantly reduce the exploration space. We demonstrate the effectiveness of our approach on graph coloring problem using probabilistic Ising solvers, achieving similar accuracy compared to state-of-the-art heuristics and machine learning algorithms. It also shows significant improvement over state-of-the-art QUBO-based Ising solvers, including probabilistic Ising and simulated bifurcation machines. We also design 1024-neuron all-to-all connected probabilistic Ising accelerator on FPGA with the proposed approach that shows ∼\sim∼ ~ 10000 ×\times× × performance acceleration compared to GPU-based Tabucol heuristics and reducing physical neurons by 1.5 −-− − 4 ×\times× × over baseline Ising frameworks. Thus, this work establishes superior efficiency, scalability and solution quality for multi-state optimization problems.