Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Frontiers in Optics

Large-scale Ising Emulator with All-to-All Connected Four-Body Interactions

He Zhang, Santosh Kumar, Yu‐Ping Huang

Published Jan 1, 2020• 0 citations
Publisher

Abstract

We propose a nonlinear-optics approach to emulate Ising machine containing a million spins with weighted all-to-all four-body interactions. Using adaptive feedback control, this machine can be evolved to find the optimum solution of Ising problem.
DOI
10.1364/fio.2020.jm6a.26

Related papers

Large-scale Ising emulation with four body interaction and all-to-all connections

Santosh Kumar, He Zhang, Yu-Ping Huang

Abstract Optical Ising machines with two-body interactions have shown potential in solving combinatorial optimization problems which are extremely hard to solve with digital computers. Yet, some physical systems cannot be properly described by only two-body interactions. Here, we propose and demonstrate a nonlinear optics approach to emulate Ising machines containing many spins (up to a million in the absence of optical imperfections) and with tailored all-to-all two and four-body interactions. Our approach employs a spatial light modulator to encode and control the spins in the form of the binary-phase values, and emulates the high-order interaction with frequency conversion in a nonlinear crystal. By implementing adaptive feedback, the system can be evolved into effective spin configurations that well-approximate the ground-states of Ising Hamiltonians with all-to-all connected many-body interactions. Our technique could serve as a tool to probe complex, many-body physics and give rise to exciting applications in big-data optimization, computing, and analytics.

arXiv:2001.05680•Communications Physics•Jun 12, 2020•58 citations

A fully programmable 100-spin coherent Ising machine with all-to-all connections

Peter L. McMahon, Alireza Marandi, Yoshitaka Haribara +11 more

Unconventional, special-purpose machines may aid in accelerating the solution of some of the hardest problems in computing, such as large-scale combinatorial optimizations, by exploiting different operating mechanisms than those of standard digital computers. We present a scalable optical processor with electronic feedback that can be realized at large scale with room-temperature technology. Our prototype machine is able to find exact solutions of, or sample good approximate solutions to, a variety of hard instances of Ising problems with up to 100 spins and 10,000 spin-spin connections.

Bulletin of the American Physical Society•Mar 13, 2017•0 citations

A fully programmable 100-spin coherent Ising machine with all-to-all connections

Peter L. McMahon, Alireza Marandi, Yoshitaka Haribara +11 more

Taking the pulse of optimization Finding the optimum solution of multiparameter or multifunctional problems is important across many disciplines, but it can be computationally intensive. Many such problems defined as computationally difficult can be mathematically mapped onto the so-called Ising problem, which looks at finding the minimum energy configuration for an array of coupled spins. Inagakiet al.and McMahonet al.show that an optical processing approach based on a network of coupled optical pulses in a ring fiber can be used to model and optimize large-scale Ising systems. Such a scalable architecture could help to optimize solutions to a wide range of complex problems. Science, this issue pp. 603 and 614

Science•Oct 21, 2016•703 citations

Photonic Ising machines toward and beyond a million spins

Abdul Rahim Aadhi, Nayem Al Kayed, Tristan Austin +8 more

Combinatorial optimization problems are central to many challenges in logistics, finance, engineering, and the life sciences, yet they remain among the most computationally demanding. Many of these problems can be mapped onto the Ising model, in which binary spins interact through a network of couplings, and solutions correspond to low-energy, ideally ground-state, spin configurations. Photonic Ising machines have the potential to be fast and energy-efficient heuristic solvers of optimization problems by leveraging the low latency, high bandwidth, and inherent parallelism of optics. However, current photonic implementations remain limited in scalability, connectivity, reconfigurability, and time-to-solution, preventing their use in many practical applications. In this perspective, we examine the current landscape of photonic Ising machines, discuss the challenges and limitations of existing platforms, and identify the scientific and technological advances needed to realize large-scale systems. These developments could establish photonic Ising machines as useful hardware platforms for practical optimization.

arXiv:2607.13446•Jul 15, 2026•0 citations

The Spatial Ising Machine

Claudio Conti

We show how spatial light modulation allows one to optically compute an Ising Hamiltonian. This enables the use of optical radiation to accelerate algorithms to find the ground state and hence the optimal solution of a combinatorial optimization problem. The phase matrix on a spatial light modulator acts as a lattice of spins whose interaction is ruled by the constrained optical intensity in the far field. Feedback from the detection plane allows the spatial phase distribution to evolve toward the minimum of the selected spin model. Related topics such as annealing, adiabatic evolution, the XY model, and decomposition methods are reviewed.

Cambridge University Press eBooks•Jun 26, 2026•0 citations

All-optical interferometer-based Ising machine

Yuan Gao, Qi Luo, Hong‐Lin Lin +2 more

Ising machines promise scaling advantages when solving highly complex combinatorial optimization problems compared to traditional computers. To fully exploit this, highly parallel physical systems must be identified. Here, we constructed and characterized an all-optical Ising machine utilizing a free-space Mach–Zehnder interferometer, which theoretically supports an unlimited number of spins and all-to-all programmable spin interactions via a matrix partitioning method. Crucially, in our proposed system, all bottleneck-producing mathematical operations like matrix-vector multiplication and nonlinear operations are performed optically in parallel, while electronic devices only need to execute simple arithmetic such as addition and storage/retrieval, ensuring fast nominal operation speeds. With periodic auto-calibration, the Ising machine has achieved over 160 h of high optical stability and accurate calculation. The machine is experimentally demonstrated with MAXCUT problems involving 1024 spins. The design itself is highly amenable to fabrication on an integrated chip-based photonics platform; a smooth trade-off exists between chip size and solution time.

Optica•Apr 18, 2025•8 citations
Abstract Optical Ising machines with two-body interactions have shown potential in solving combinatorial optimization problems which are extremely hard to solve with digital computers. Yet, some physical systems cannot be properly described by only two-body interactions. Here, we propose and demonstrate a nonlinear optics approach to emulate Ising machines containing many spins (up to a million in the absence of optical imperfections) and with tailored all-to-all two and four-body interactions. Our approach employs a spatial light modulator to encode and control the spins in the form of the binary-phase values, and emulates the high-order interaction with frequency conversion in a nonlinear crystal. By implementing adaptive feedback, the system can be evolved into effective spin configurations that well-approximate the ground-states of Ising Hamiltonians with all-to-all connected many-body interactions. Our technique could serve as a tool to probe complex, many-body physics and give rise to exciting applications in big-data optimization, computing, and analytics.
Unconventional, special-purpose machines may aid in accelerating the solution of some of the hardest problems in computing, such as large-scale combinatorial optimizations, by exploiting different operating mechanisms than those of standard digital computers. We present a scalable optical processor with electronic feedback that can be realized at large scale with room-temperature technology. Our prototype machine is able to find exact solutions of, or sample good approximate solutions to, a variety of hard instances of Ising problems with up to 100 spins and 10,000 spin-spin connections.
Taking the pulse of optimization Finding the optimum solution of multiparameter or multifunctional problems is important across many disciplines, but it can be computationally intensive. Many such problems defined as computationally difficult can be mathematically mapped onto the so-called Ising problem, which looks at finding the minimum energy configuration for an array of coupled spins. Inagakiet al.and McMahonet al.show that an optical processing approach based on a network of coupled optical pulses in a ring fiber can be used to model and optimize large-scale Ising systems. Such a scalable architecture could help to optimize solutions to a wide range of complex problems. Science, this issue pp. 603 and 614
Combinatorial optimization problems are central to many challenges in logistics, finance, engineering, and the life sciences, yet they remain among the most computationally demanding. Many of these problems can be mapped onto the Ising model, in which binary spins interact through a network of couplings, and solutions correspond to low-energy, ideally ground-state, spin configurations. Photonic Ising machines have the potential to be fast and energy-efficient heuristic solvers of optimization problems by leveraging the low latency, high bandwidth, and inherent parallelism of optics. However, current photonic implementations remain limited in scalability, connectivity, reconfigurability, and time-to-solution, preventing their use in many practical applications. In this perspective, we examine the current landscape of photonic Ising machines, discuss the challenges and limitations of existing platforms, and identify the scientific and technological advances needed to realize large-scale systems. These developments could establish photonic Ising machines as useful hardware platforms for practical optimization.
We show how spatial light modulation allows one to optically compute an Ising Hamiltonian. This enables the use of optical radiation to accelerate algorithms to find the ground state and hence the optimal solution of a combinatorial optimization problem. The phase matrix on a spatial light modulator acts as a lattice of spins whose interaction is ruled by the constrained optical intensity in the far field. Feedback from the detection plane allows the spatial phase distribution to evolve toward the minimum of the selected spin model. Related topics such as annealing, adiabatic evolution, the XY model, and decomposition methods are reviewed.
Ising machines promise scaling advantages when solving highly complex combinatorial optimization problems compared to traditional computers. To fully exploit this, highly parallel physical systems must be identified. Here, we constructed and characterized an all-optical Ising machine utilizing a free-space Mach–Zehnder interferometer, which theoretically supports an unlimited number of spins and all-to-all programmable spin interactions via a matrix partitioning method. Crucially, in our proposed system, all bottleneck-producing mathematical operations like matrix-vector multiplication and nonlinear operations are performed optically in parallel, while electronic devices only need to execute simple arithmetic such as addition and storage/retrieval, ensuring fast nominal operation speeds. With periodic auto-calibration, the Ising machine has achieved over 160 h of high optical stability and accurate calculation. The machine is experimentally demonstrated with MAXCUT problems involving 1024 spins. The design itself is highly amenable to fabrication on an integrated chip-based photonics platform; a smooth trade-off exists between chip size and solution time.