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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.
Nature Communications
|April 17, 2026
Summary
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.
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.
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