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Related papers: Noise-augmented Chaotic Ising Machines for Combina…

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Inspired by the developments in quantum computing, building domain-specific classical hardware to solve computationally hard problems has received increasing attention. Here, by introducing systematic sparsification techniques, we…

Analog computing using bosonic computational states is a leading approach to surpassing the computational speed and energy limitations of von Neumann architectures. But the challenges of manufacturing large-scale photonic integrated…

Optics · Physics 2025-07-22 Farhad Khosravi , Martin Perreault , Artur Scherer , Pooya Ronagh

Domain-specific hardware to solve computationally hard optimization problems has generated tremendous excitement. Here, we evaluate probabilistic bit (p-bit) based Ising Machines (IM) on the 3-regular 3-Exclusive OR Satisfiability (3R3X),…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-10-21 Srijan Nikhar , Sidharth Kannan , Navid Anjum Aadit , Shuvro Chowdhury , Kerem Y. Camsari

The commercial and industrial demand for the solution of hard combinatorial optimization problems push forward the development of efficient solvers. One of them is the Ising machine which can solve combinatorial problems mapped to Ising…

As we approach the physical limits predicted by Moore's law, a variety of specialized hardware is emerging to tackle specialized tasks in different domains. Within combinatorial optimization, adiabatic quantum computers, CMOS annealers, and…

Data Structures and Algorithms · Computer Science 2020-12-01 Xiaoyuan Liu , Hayato Ushijima-Mwesigwa , Avradip Mandal , Sarvagya Upadhyay , Ilya Safro , Arnab Roy

Spiking neural network is a kind of neuromorphic computing that is believed to improve the level of intelligence and provide advantages for quantum computing. In this work, we address this issue by designing an optical spiking neural…

Quantum Physics · Physics 2023-10-25 Bo Lu , Yong-Pan Gao , Kai Wen , Chuan Wang

Many combinatorial optimization problems can be mapped to finding the ground states of the corresponding Ising Hamiltonians. The physical systems that can solve optimization problems in this way, namely Ising machines, have been attracting…

Emerging Technologies · Computer Science 2017-10-16 Tianshi Wang , Jaijeet Roychowdhury

Decades of exponential scaling in high performance computing (HPC) efficiency is coming to an end. Transistor based logic in complementary metal-oxide semiconductor (CMOS) technology is approaching physical limits beyond which further…

Machine Learning · Computer Science 2024-02-01 Fiona Knoll , John T. Daly , Jess J. Meyer

Interest in non-algorithmic, unconventional computing is rising in recent years due to more and more apparent short comings of classic stored-program digital computers, such as energy efficiency, degree of parallelism in computations, clock…

Emerging Technologies · Computer Science 2025-02-07 Shrish Roy , Bernd Ulmann

Ising machines (IM) are physics-inspired alternatives to von Neumann architectures for solving hard optimization tasks. By mapping binary variables to coupled Ising spins, IMs can naturally solve unconstrained combinatorial optimization…

Emerging Technologies · Computer Science 2025-08-01 Corentin Delacour

Coherent Ising Machines (CIMs) have emerged as a hybrid form of quantum computing devices designed to solve NP-complete problems, offering an exciting opportunity for discovering optimal solutions. Despite challenges such as susceptibility…

Adiabatic Quantum Computing relies on the quantum adiabatic theorem, which states that a quantum system evolves along its ground state with time if the governing Hamiltonian varies infinitely slowly. However, practical limitations force…

The recent emergence of novel computational devices, such as adiabatic quantum computers, CMOS annealers, and optical parametric oscillators, present new opportunities for hybrid-optimization algorithms that are hardware accelerated by…

Optimization and Control · Mathematics 2019-06-20 Carleton Coffrin , Harsha Nagarajan , Russell Bent

We present a stochastic algorithm for constructing a topologically disordered (i.e., non-regular) spatial lattice with nodes of constant coordination number, the CC lattice. The construction procedure dramatically improves on an earlier…

Disordered Systems and Neural Networks · Physics 2019-11-06 Manuel Schrauth , Jefferson S. E. Portela

The Coherent Ising Machine (CIM) is a non-conventional architecture that takes inspiration from physical annealing processes to solve Ising problems heuristically. Its dynamics are naturally continuous and described by a set of ordinary…

Optimization and Control · Mathematics 2024-01-23 Robin Brown , Davide Venturelli , Marco Pavone , David E. Bernal Neira

The Ising machine is an unconventional computing architecture that can be used to solve NP-hard combinatorial optimization problems more efficiently than traditional von Neumann architectures. Fast, compact oscillator networks which provide…

Computational Physics · Physics 2021-10-20 Brooke C. McGoldrick , Jonathan Z. Sun , Luqiao Liu

Analog Ising machines are dedicated hardware solvers designed to solve NP hard optimization problems. However, the global optimum is often not found as the system gets stuck in local minima. While several strategies exist to increase the…

Applied Physics · Physics 2026-04-03 Leen Mys , Guy Verschaffelt , Guy Van der Sande

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…

Probabilistic Bit (P-Bit) device serves as the core hardware for implementing Ising computation. However, the severe intrinsic variations of stochastic P-Bit devices hinder the large-scale expansion of the P-Bit array, significantly…

Mesoscale and Nanoscale Physics · Physics 2024-10-23 Bolin Zhang , Yu Liu , Tianqi Gao , Jialiang Yin , Zhenyu Guan , Deming Zhang , Lang Zeng

Analog Ising machines (AIMs) have emerged as a promising paradigm for combinatorial optimization, utilizing physical dynamics to solve Ising problems with high energy efficiency. However, the performance of traditional optimization and…

Machine Learning · Computer Science 2026-02-11 Aditya Choudhary , Saaketh Desai , Prasad Iyer