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

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Probabilistic computing, a physics-based approach, offers efficient solutions for complex NP problems by analyzing the Ising model. This study extends binary systems to multi-level frameworks for advanced computational challenges.

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Area of Science:

  • Physics-based computing
  • Computational complexity theory
  • Information science

Background:

  • Classical von Neumann architecture faces limitations with NP-hard problems.
  • Quantum computing offers potential but has its own challenges.
  • Probabilistic computing emerges as a promising alternative, bridging classical and quantum paradigms.

Purpose of the Study:

  • To analyze the fundamental principles of probabilistic computing, focusing on the Ising model.
  • To investigate bit fluctuations and energy trends within this framework.
  • To extend probabilistic computing from binary to multi-level systems, using number partitioning as a case study.

Main Methods:

  • Analysis of the Ising model framework for probabilistic computing.
  • Examination of bit fluctuations and energy dynamics.
  • Extension of the binary system to a multi-level probabilistic framework.

Main Results:

  • Detailed analysis of the Ising model's role in probabilistic computing.
  • Demonstration of extending binary probabilistic systems to multi-level scenarios.
  • Case study illustrating the application to the multiway number partitioning problem.

Conclusions:

  • Probabilistic computing, grounded in physics and the Ising model, provides an efficient computational approach.
  • The framework is adaptable beyond binary systems, showing potential for complex multi-level problems.
  • This research contributes to advancing physics-based computing for tackling challenging computational tasks.