Probabilistic computing with p-bits is a powerful, unique paradigm alternative to classical computing and holds experimental advantages over certain forms of quantum computing. Stochastic nanodevices have been experimentally demonstrated to act as artificial neurons in solving certain problems through probabilistic computing. Still, many open questions about the breadth and size of soluble problems remain. We demonstrate the capability of probabilistic computing made of a stochastic nanodevice network in solving likely NP (non-deterministic polynomial time)-complete number theory problems associated with combinatorial optimization, which can be implemented using a network of optical parametric oscillators. These simulation results show robustness across all problems tested, with great potential to scale to solve substantially larger problems.

中文翻译：

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NP完全数论问题概率计算的数值模拟

使用 p 位的概率计算是一种强大、独特的范例，可替代经典计算，并且相对于某些形式的量子计算具有实验优势。随机纳米设备已通过实验证明可以充当人工神经元，通过概率计算解决某些问题。尽管如此，关于可解决问题的广度和规模的许多悬而未决的问题仍然存在。我们展示了由随机纳米设备网络构成的概率计算在解决可能的 NP（非确定性多项式时间）时的能力 - 与组合优化相关的完整数论问题，可以使用光学参量振荡器网络实现。这些模拟结果显示了所有测试问题的稳健性，具有扩展以解决更大问题的巨大潜力。