A parallel magnetic tunnel junction-based probabilistic Ising processor for efficient quadratic optimization

Solving computationally demanding combinatorial optimization problems using conventional computing architectures is slow and energy intensive. Quantum computing could improve optimization efficiency but remains at an early stage. Probabilistic computing offers a practical near-term approach to faster optimization through stochastic techniques. Here, we experimentally demonstrate a scalable spin-transfer-torque-magnetic-tunnel-junction based probabilistic processor for efficiently solving all-to-all connected quadratic assignment problems. Our system integrates 144 compact spintronics tunable random number generators with a massively parallel architecture, achieving a high Monte-Carlo sampling throughput of 14.4 million flips per second. We co-design a parallel trial annealing scheme, and the integrated system achieves a 123× speedup with 98.3% energy savings over conventional Gibbs sampling, and a 3.2× speedup with 58.3% energy savings relative to the central processing unit implementation based on a compiled language. We further benchmark performance across graphics processing unit, and D-Wave quantum annealers, showing gains in solution quality, speed, and energy efficiency.