Point convolutional neural network algorithm for Ising model ground state research based on spring vibration
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 conv...
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| Published in | Scientific reports Vol. 14; no. 1; pp. 2643 - 11 |
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| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
01.02.2024
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2045-2322 2045-2322 |
| DOI | 10.1038/s41598-023-49559-3 |
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| 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 the springs, we propose the Spring-Ising Algorithm, a point convolutional neural network algorithm for ground state search based on the spring vibration model. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establishes the equation of motion for all spins. Spring-Ising Algorithm can be mapped on AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has shown promising results in solving the Ising model and has been tested in the recognized test benchmark K2000. The optimal results of this algorithm after 10,000 steps of iteration are 2.9% of all results. 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. Spring-Ising Algorithm offers the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations. |
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| AbstractList | 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 the springs, we propose the Spring-Ising Algorithm, a point convolutional neural network algorithm for ground state search based on the spring vibration model. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establishes the equation of motion for all spins. Spring-Ising Algorithm can be mapped on AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has shown promising results in solving the Ising model and has been tested in the recognized test benchmark K2000. The optimal results of this algorithm after 10,000 steps of iteration are 2.9% of all results. 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. Spring-Ising Algorithm offers the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations. 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 the springs, we propose the Spring-Ising Algorithm, a point convolutional neural network algorithm for ground state search based on the spring vibration model. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establishes the equation of motion for all spins. Spring-Ising Algorithm can be mapped on AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has shown promising results in solving the Ising model and has been tested in the recognized test benchmark K2000. The optimal results of this algorithm after 10,000 steps of iteration are 2.9% of all results. 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. Spring-Ising Algorithm offers the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations.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 the springs, we propose the Spring-Ising Algorithm, a point convolutional neural network algorithm for ground state search based on the spring vibration model. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establishes the equation of motion for all spins. Spring-Ising Algorithm can be mapped on AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has shown promising results in solving the Ising model and has been tested in the recognized test benchmark K2000. The optimal results of this algorithm after 10,000 steps of iteration are 2.9% of all results. 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. Spring-Ising Algorithm offers the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations. 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 the springs, we propose the Spring-Ising Algorithm, a point convolutional neural network algorithm for ground state search based on the spring vibration model. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establishes the equation of motion for all spins. Spring-Ising Algorithm can be mapped on AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has shown promising results in solving the Ising model and has been tested in the recognized test benchmark K2000. The optimal results of this algorithm after 10,000 steps of iteration are 2.9% of all results. 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. Spring-Ising Algorithm offers the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations. |
| ArticleNumber | 2643 |
| Author | Li, Zhigang Chen, Yihao Lu, Huaxiang Qiao, Ruixiu Chen, Gang Su, Junjia Zhao, Zhiyuan Jin, Min Jiang, Zhelong Chen, Xu Feng, Pengcheng |
| Author_xml | – sequence: 1 givenname: Zhelong surname: Jiang fullname: Jiang, Zhelong organization: Institute of Semiconductors, Chinese Academy of Sciences, Materials and Optoelectronics Research Center, University of Chinese Academy of Sciences – sequence: 2 givenname: Gang surname: Chen fullname: Chen, Gang email: chengang08@semi.ac.cn organization: Institute of Semiconductors, Chinese Academy of Sciences – sequence: 3 givenname: Ruixiu surname: Qiao fullname: Qiao, Ruixiu organization: Institute of Semiconductors, Chinese Academy of Sciences – sequence: 4 givenname: Pengcheng surname: Feng fullname: Feng, Pengcheng organization: Institute of Semiconductors, Chinese Academy of Sciences, Materials and Optoelectronics Research Center, University of Chinese Academy of Sciences – sequence: 5 givenname: Yihao surname: Chen fullname: Chen, Yihao organization: Institute of Semiconductors, Chinese Academy of Sciences, Materials and Optoelectronics Research Center, University of Chinese Academy of Sciences – sequence: 6 givenname: Junjia surname: Su fullname: Su, Junjia organization: Institute of Semiconductors, Chinese Academy of Sciences, Materials and Optoelectronics Research Center, University of Chinese Academy of Sciences – sequence: 7 givenname: Zhiyuan surname: Zhao fullname: Zhao, Zhiyuan organization: Institute of Semiconductors, Chinese Academy of Sciences, School of Microelectronics, University of Science and Technology of China – sequence: 8 givenname: Min surname: Jin fullname: Jin, Min organization: Institute of Semiconductors, Chinese Academy of Sciences – sequence: 9 givenname: Xu surname: Chen fullname: Chen, Xu organization: Institute of Semiconductors, Chinese Academy of Sciences – sequence: 10 givenname: Zhigang surname: Li fullname: Li, Zhigang organization: Institute of Semiconductors, Chinese Academy of Sciences – sequence: 11 givenname: Huaxiang surname: Lu fullname: Lu, Huaxiang organization: Institute of Semiconductors, Chinese Academy of Sciences, Materials and Optoelectronics Research Center, University of Chinese Academy of Sciences, College of Microelectronics, University of Chinese Academy of Sciences, Semiconductor Neural Network Intelligent Perception and Computing Technology Beijing Key Laboratory |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38302489$$D View this record in MEDLINE/PubMed |
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| Snippet | The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising... Abstract The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an... |
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| SubjectTerms | 639/705 639/766 Algorithms Energy conversion Humanities and Social Sciences multidisciplinary Neural networks Potential energy Science Science (multidisciplinary) |
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| Title | Point convolutional neural network algorithm for Ising model ground state research based on spring vibration |
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