22 papers · ranked by Valyu relevance
Connor Bybee, Denis Kleyko, Dmitri E. Nikonov, Amir Khosrowshahi + 2 more
A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i.e., hardware implementations of networks of interacting binary spin variables. Most Ising machines leverage second-order interactions although important classes of optimization problems, such as satisfiability…
L. Schmid, Enrico Zardini, Davide Pastorello
An Ising machine is any hardware specifically designed for finding the ground state of the Ising model. Relevant examples are coherent Ising machines and quantum annealers. In this paper, we propose a new machine learning model that is based on the Ising structure and can be efficiently trained using gradient descent.…
Tianshi Wang, Jaijeet Roychowdhury
—Many combinatorial optimization problems can be mapped to finding the ground states of the corresponding Isin g Hamiltonians. The physical systems that can solve optimization problems in this way, namely Ising machines, have been attracting more and more attention recently. Our work shows that Ising machines can be…
Tianshi Wang, Jaijeet Roychowdhury
We present a new way to make Ising machines, i.e., using networks of coupled self-sustaining nonlinear oscillators. Our scheme is theoretically rooted in a novel result that establishes that the phase dynamics of coupled oscillator systems, under the influence of subharmonic injection locking, are governed by a…
Yi Zhang, Yi Deng, Yinan Lin, Yang Jiang + 8 more
'Guangyi Wang' 'Dashan Shang' 'Qing Wang' 'Hongyu Yu' 'Zhongrui Wang' 'Aiqun Liu'] With the slowdown of Moore’s law, many emerging electronic devices and computing architectures have been proposed to sustain the performance advancement of computing. Among them, the Ising machine is a non-von-Neumann solver that has…
Qizhuang Cen, Hao Ding, Tengfei Hao, Shanhong Guan + 7 more
'Jiaming Lyu' 'Wei Li' 'Ninghua Zhu' 'Kun Xu' 'Yitang Dai' 'Ming Li'] Ising machines based on analog systems have the potential to accelerate the solution of ubiquitous combinatorial optimization problems. Although some artificial spins to support large-scale Ising machines have been reported, e.g., superconducting…
Motohiko Ezawa, Éric Lebrasseur, Yoshio Mita
We propose an Ising machine made of microelectromechanical systems (MEMS), where the annealing process is automatically executed by a dissipation mechanism. The core structure is a series of buckled plates. Two stable positions of each plate (left and right) represent its binary state acting as a bit so that a plate…
Naeimeh Mohseni, Peter L. McMahon, Tim Byrnes
Ising machines are hardware solvers which aim to find the absolute or approximate ground states of the Ising model. The Ising model is of fundamental computational interest because it is possible to formulate any problem in the complexity class NP as an Ising problem with only polynomial overhead. A scalable Ising…
Anshujit Sharma, Matthew Burns, Andrew Hahn, Michael Huang
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.…
Mohammed Akib Iftakher, Hugo Levices, Kamel-Eddine Harabi, Adrien Renaudineau + 9 more
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 effectiveness. Other nanodevices have been shown to implement spin-spin coupling with…
Jacob Lamers, Guy Verschaffelt, Guy Van der Sande
by Ising machines Authors: ['Jacob Lamers' 'Guy Verschaffelt' 'Guy Van der Sande'] Ising machines are dedicated hardware solvers of NPhard optimization problems. However, they do not always find the most optimal solution. The probability of finding this optimal solution depends on the problem at hand. Using…
Jérémie Laydevant, Danijela Marković, Julie Grollier
Ising machines, which are hardware implementations of the Ising model of coupled spins, have been influential in the development of unsupervised learning algorithms at the origins of Artificial Intelligence (AI). However, their application to AI has been limited due to the complexities in matching supervised training…
Fabian Böhm, Diego Alonso-Urquijo, Guy Verschaffelt, Guy Van der Sande
'Guy Van der Sande'] Ising machines are a promising non-von-Neumann computational concept for neural network training and combinatorial optimization. However, while various neural networks can be implemented with Ising machines, their inability to perform fast statistical sampling makes them inefficient for training…
Fabian Böhm, Diego Alonso-Urquijo, Guy Verschaffelt, Guy Van der Sande
'Guy Van der Sande'] Ising machines are a promising non-von-Neumann computational concept for neural network training and combinatorial optimization. However, while various neural networks can be implemented with Ising machines, their inability to perform fast statistical sampling makes them inefficient for training…
Kai Wen, Jinyin Zha, Shaobo Chen, Jie Zhong + 26 more
Coherent Ising machines (CIMs) excel at solving large-scale combinational optimization problems (COPs), but their insufficient long-term stability has hindered their applications in compute-intensive tasks like computer-aided drug discovery (CADD). By improving fiber vibration isolation and temperature control system…
Authors not listed
Stable proton configurations in solid-state materials are a prerequisite for the theoretical microscopic investigation of solid-state proton-conductive materials. However, a large number of initial atomistic configurations should be considered to find stable proton configurations, and relaxation calculations using the…
Kinjal Mondal, Jeffery B. Klauda
Clustering is a type of machine learning (ML) technique which is used to group huge amounts of data based on their similarity into separate groups or clusters. Clustering is a very important task which is nowadays used to analyze the huge and diverse amount of data coming out of molecular dynamics (MD) simulations.…
T. S. A. N. Simões, C. I. N. Sampaio Filho, H. J. Herrmann, J. S. Andrade + 1 more
Relying on maximum entropy arguments, certain aspects of time-averaged experimental neuronal data have been recently described using Ising-like models, allowing the study of neuronal networks under an analogous thermodynamical framework. Here, we apply for the first time the Maximum Entropy method to an…
Authors not listed
Chemical reactions are regarded as transformations of chemical structures, and the question of which atoms in the reactants correspond to which atoms in the products has attracted chemists for a long time. Atom-to-atom mapping (AAM) is a procedure that establishes such correspondence(s) between the atoms of reactants…
Rodrigo M. Cabral-Carvalho, Walter H. L. Pinaya, João R. Sato
Recent studies show that functional resting-state dynamics may be modelled by lattice models near criticality, such as the 2D Ising model. The Ising temperature, which is the control parameter dictating the phase transitions of the model, can provide insight into the large-scale dynamics and is being used to better…
Steven William Rutherford
Hydrophobic and hydrophilic phenomena displayed by microporous materials are crucial to a wide range of biological and energy storage technologies and are foundational to addressing the water-energy nexus. However, advancement of fundamental understanding is impeded by the failure of classical analyses to identify…
Authors not listed
Graphite is a layered material with applications in nuclear reactors, electronics, and batteries. As a consequence of the weak van der Waals interactions between the layers, their stacking arrangement is often disordered which in turn affects the mechanical, electronic and optical properties of graphite. In this work…