Finding a reliable Ising machine for solving nondeterministic polynomial-class problems has attracted great attention in recent years, where an authentic system can be expanded with polynomial-scaled resources to find the ground state Ising Hamiltonian. In this Letter, we propose an extremely low power optomechanical coherent Ising machine based on a new enhanced symmetry breaking mechanism and highly nonlinear mechanical Kerr effect. The mechanical movement of an optomechanical actuator induced by the optical gradient force greatly increases the nonlinearity by a few orders and significantly reduces the power threshold using conventional structures capable of fabrication via photonic integrated circuit platforms. With the simple but strong bifurcation mechanism and remarkably low power requirement, our optomechanical spin model opens a path for chip-scale integration of large-size Ising machine implementations with great stability.
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http://dx.doi.org/10.1103/PhysRevLett.130.073802 | DOI Listing |
Entropy (Basel)
November 2024
Beijing QBoson Quantum Technology Co., Ltd., Beijing 100015, China.
Fraud detection within transaction data is crucial for maintaining financial security, especially in the era of big data. This paper introduces a novel fraud detection method that utilizes quantum computing to implement community detection in transaction networks. We model transaction data as an undirected graph, where nodes represent accounts and edges indicate transactions between them.
View Article and Find Full Text PDFJ Phys Chem B
January 2025
Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.
Machine learning methods have been important in the study of phase transitions. Unsupervised methods are particularly attractive because they do not require prior knowledge of the existence of a phase transition. In this work we focus on the constant magnetization Ising model in two (2D) and three (3D) dimensions.
View Article and Find Full Text PDFJ Chem Phys
December 2024
Institute for Physical Science and Technology, Biophysics Program, University of Maryland, College Park, Maryland 20742, USA.
Clustering is a type of machine learning 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 that is nowadays used to analyze the huge and diverse amount of data coming out of molecular dynamics (MD) simulations. Typically, the data from the MD simulations in terms of their various frames in the trajectory are clustered into different groups and a representative element from each group is studied separately.
View Article and Find Full Text PDFJ Comput Cogn Eng
November 2024
Department of Computer Science, Utah Valley University, USA.
A restricted Boltzmann machine is a fully connected shallow neural network. It can be used to solve many challenging optimization problems. The Boltzmann machines are usually considered probability models.
View Article and Find Full Text PDFNanophotonics
May 2024
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA.
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