Point convolutional neural network algorithm for Ising model ground state research based on spring vibration

Author:

Jiang Zhelong1,Chen Gang1,Qiao Ruixiu1,Feng Pengcheng1,Chen Yihao1,Su Junjia1,Zhao Zhiyuan2,Jin Min1,Chen Xu1,Li Zhigang1,Lu Huaxiang1

Affiliation:

1. Chinese Academy of Sciences

2. University of Science and Technology of China

Abstract

Abstract The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of springs, we propose a point convolutional neural network algorithm for ground state search based on spring vibration model, called Spring-Ising Algorithm. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establish the equation of motion for all spins. Spring-Ising Algorithm can be mapped on the GPU or AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has very productive results for solving the Ising model and has been test in the recognized test benchmark K2000. The algorithm introduces the concept of dynamic equilibrium to achieve a more detailed local search by dynamically adjusting the weight of the Ising model in the spring oscillation model. Finally, there is the simple hardware test speed evaluation. Spring-Ising Algorithm can provide the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations.

Publisher

Research Square Platform LLC

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