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<p>Probabilistic computing, a class of physics-based computing, bridges the gap between quantum computing and the classical von Neumann architecture. This approach provides more efficient means of addressing NP problems, which are challenging for classical computers. In this work, we analyze the core concept of probabilistic computing which is based on the Ising model framework—including bit fluctuations and energy trends. In addition, we extend the traditional binary (two-level) system into a multi-level probabilistic framework, i.e. number partitioning problem to multiway number partitioning problem, as a case study.</p>

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Multi-level probabilistic computing: application to the multiway number partitioning problems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月7日

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  • Ki Hyuk Han,
  • Gyuyoung Park,
  • Jeong Ung Ahn,
  • YongJin Kim,
  • Deok Hyun Yun,
  • Seung-Hun Jang,
  • Young-Jun Nah,
  • Min-Gu Kang,
  • Hyun Cheol Koo,
  • OukJae Lee

摘要

Probabilistic computing, a class of physics-based computing, bridges the gap between quantum computing and the classical von Neumann architecture. This approach provides more efficient means of addressing NP problems, which are challenging for classical computers. In this work, we analyze the core concept of probabilistic computing which is based on the Ising model framework—including bit fluctuations and energy trends. In addition, we extend the traditional binary (two-level) system into a multi-level probabilistic framework, i.e. number partitioning problem to multiway number partitioning problem, as a case study.