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250 magnetic tunnel junctions-based probabilistic Ising machine.

Shuhan Yang1, Andrea Grimaldi2,3, Youwei Bao1

  • 1Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.

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|April 17, 2026
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Summary
This summary is machine-generated.

This study demonstrates a scalable probabilistic Ising machine using magnetic tunnel junctions, accelerating Monte Carlo sampling by 10x and improving solution quality 20x for combinatorial optimization problems.

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Area of Science:

  • Spintronics
  • Combinatorial Optimization
  • Neuromorphic Computing

Background:

  • Probabilistic Ising machines accelerate Monte Carlo sampling for optimization.
  • Scalability and energy efficiency are key challenges for real-world applications.

Purpose of the Study:

  • To experimentally demonstrate a scalable probabilistic Ising machine.
  • To improve the speed and solution quality of combinatorial optimization.

Main Methods:

  • Utilized 250 spin-transfer-torque magnetic tunnel junctions.
  • Integrated spintronic tunable true random number generators with advanced annealing techniques.
  • Implemented a cluster parallel update method for sparsely connected graphs.

Main Results:

  • Achieved a 10x acceleration in sampling speed compared to serial Gibbs sampling.
  • Demonstrated a 20x boost in solution quality using simulated quantum annealing over conventional simulated annealing.
  • Showcased increased robustness to device variability.

Conclusions:

  • The developed architecture offers a scalable and energy-efficient approach for unconventional computing.
  • A chiplet-based architecture is proposed for future large-scale implementations.
  • This work advances the potential of probabilistic Ising machines for practical applications.