Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月30日

查看全文
  • Shuhan Yang,
  • Youwei Bao,
  • Edward Humianto,
  • Anil Prabhakar,
  • Hyunsoo Yang

摘要

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.