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Hardware implementations of the Ising model offer promising solutions to large-scale optimization tasks. In the literature, various nanodevices have been shown to emulate the spin dynamics for such Ising machines with remarkable…

The inversion of a spin valve device is proposed. Opposite to a conventional spin valve of a non-magnetic spacer sandwiched between two ferromagnetic metals, an inverse spin valve is a ferromagnet sandwiched between two non-magnetic metals.…

Mesoscale and Nanoscale Physics · Physics 2008-01-14 X. R. Wang

Ising machines can solve combinatorial optimization problems by representing them as energy minimization problems. A common implementation is the probabilistic Ising machine (PIM), which uses probabilistic (p-) bits to represent coupled…

We report on a new class of Ising Machines (IMs) that rely on coupled parametric frequency dividers (PFDs) as macroscopic artificial spins. Unlike the IM counterparts based on subharmonic injection locking (SHIL), PFD IMs do not require…

The last couple of years have seen an emergence of physics-inspired computing for maximum likelihood MIMO detection. These methods involve transforming the MIMO detection problem into an Ising minimization problem, which can then be solved…

Networking and Internet Architecture · Computer Science 2023-01-18 Abhishek Kumar Singh , Davide Venturelli , Kyle Jamieson

The coherent Ising machine (CIM) is a nonconventional hardware architecture for finding approximate solutions to large-scale combinatorial optimization problems. It operates by annealing a laser gain parameter to adiabatically deform a…

Disordered Systems and Neural Networks · Physics 2025-10-28 Federico Ghimenti , Adithya Sriram , Atsushi Yamamura , Hideo Mabuchi , Surya Ganguli

We propose and demonstrate a nonlinear optics approach to emulate Ising machines containing up to a million spins and with tailored two and four-body interactions with all-to-all connections. It uses a spatial light modulator to encode and…

Optics · Physics 2020-10-21 Santosh Kumar , He Zhang , Yu-Ping Huang

As a quantum-inspired, non-traditional analog solver architecture, the analog Ising machine (AIM) has emerged as a distinctive computational paradigm to address the rapidly growing demand for computational power. However, the mathematical…

Quantum Physics · Physics 2026-02-06 Langyu Li , Ruoyu Wu , Yong Wang , Guofeng Zhang , Jinhu Lü , Qing Gao , Yu Pan

Coherent Ising Machine (CIM) is a network of optical parametric oscillators that solves combinatorial optimization problems by finding the ground state of an Ising Hamiltonian. As a practical application of CIM, Aonishi et al. proposed a…

Spatial photonic Ising machines offer a novel optical platform for optimization and spin-model simulation, but existing diffraction-based schemes rely on auxiliary spins or multiplexing to encode high-rank couplings and external fields,…

Optics · Physics 2026-05-12 Ze Zheng , Yuegang Li , Hang Xu , Jingzheng Huang , Tailong Xiao , Guihua Zeng

Spin Hamiltonian engineering in solid-state systems plays a key role in a variety of applications ranging from quantum information processing and quantum simulations to novel studies of many-body physics. By analyzing the irreducible form…

Quantum Physics · Physics 2020-02-04 K. I. O. Ben 'Attar , D. Farfurnik , N. Bar-Gill

Semiconductor holes with strong spin-orbit coupling allow all-electrical spin control, with broad applications ranging from spintronics to quantum computation. Using a two-dimensional hole system in a GaAs quantum well, we demonstrate a new…

From condensed matter to quantum chromodynamics, multidimensional spins are a fundamental paradigm, with a pivotal role in combinatorial optimization and machine learning. Machines formed by coupled parametric oscillators can simulate spin…

Optics · Physics 2022-11-29 Marcello Calvanese Strinati , Claudio Conti

Stacked intelligent metasurfaces (SIMs) have recently emerged as an effective solution for next-generation wireless networks. A SIM comprises multiple metasurface layers that enable signal processing directly in the wave domain. Moreover,…

Signal Processing · Electrical Eng. & Systems 2025-11-11 Ahmed Magbool , Vaibhav Kumar , Marco Di Renzo , Mark F. Flanagan

Quantum or quantum-inspired Ising machines have recently shown promise in solving combinatorial optimization problems in a short time. Real-world applications, such as time division multiple access (TDMA) scheduling for wireless multi-hop…

Emerging Technologies · Computer Science 2025-04-03 Yohei Hamakawa , Tomoya Kashimata , Masaya Yamasaki , Kosuke Tatsumura

In this paper, we report new results on a novel Ising machine technology for solving combinatorial optimization problems using networks of coupled self-sustaining oscillators. Specifically, we present several working hardware prototypes…

Emerging Technologies · Computer Science 2019-04-24 Tianshi Wang , Leon Wu , Jaijeet Roychowdhury

Oscillator Ising Machines (OIMs) and probabilistic bit (p-bit)-based computing platforms have emerged as promising paradigms for tackling complex combinatorial optimization problems. Although traditionally viewed as distinct approaches,…

Computational Physics · Physics 2026-01-26 E. M. Hasantha Ekanayake , Nikhat Khan , Nikhil Shukla

This paper presents a CMOS-compatible Lechner-Hauke-Zoller (LHZ)--based analog tile structure as a fundamental unit for developing scalable analog Ising machines (IMs). In the designed LHZ tile, the voltage-controlled oscillators are…

Hardware Architecture · Computer Science 2024-11-27 Sasan Razmkhah , Jui-Yu Huang , Mehdi Kamal , Massoud Pedram

Computationally hard combinatorial optimization problems are pervasive in science and engineering, yet their NP-hard nature renders them increasingly inefficient to solve on conventional von Neumann architectures as problem size grows.…

Emerging Technologies · Computer Science 2025-12-22 Yu Qian , Alptekin Vardar , Konrad Seidel , David Lehninger , Maximilian Lederer , Zhiguo Shi , Cheng Zhuo , Kai Ni , Thomas Kämpfe , Xunzhao Yin

Spin systems with frustration and disorder are notoriously difficult to study both analytically and numerically. While the simulation of ferromagnetic statistical mechanical models benefits greatly from cluster algorithms, these accelerated…

Disordered Systems and Neural Networks · Physics 2015-08-18 Zheng Zhu , Andrew J. Ochoa , Helmut G. Katzgraber