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The ground state search of the Ising model can be used to solve manycombinatorial optimization problems. Under the current computer architecture,an Ising ground state search algorithm suitable for hardware computing isnecessary for solving practical problems. Inspired by the potential energyconversion of springs, we propose a point convolutional neural networkalgorithm for ground state search based on spring vibration model, calledSpring-Ising Algorithm. Spring-Ising Algorithm regards the spin as a movingmass point connected to a spring and establish the equation of motion for allspins. Spring-Ising Algorithm can be mapped on the GPU or AI chips through thebasic structure of the neural network for fast and efficient parallelcomputing. The algorithm has very productive results for solving the Isingmodel and has been test in the recognized test benchmark K2000. The algorithmintroduces the concept of dynamic equilibrium to achieve a more detailed localsearch by dynamically adjusting the weight of the Ising model in the springoscillation model. Finally, there is the simple hardware test speed evaluation.Spring-Ising Algorithm can provide the possibility to calculate the Ising modelon a chip which focuses on accelerating neural network calculations.

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