Abstract Finding the solution to a large category of optimization problems, known as the NP-hard class, requires an exponentially increasing solution time using conventional computers. Lately, there has been intense efforts to develop alternative computational methods capable of addressing such tasks. In this regard, spin Hamiltonians, which originally arose in describing exchange interactions in magnetic materials, have recently been pursued as a powerful computational tool. Along these lines, it has been shown that solving NP-hard problems can be effectively mapped into finding the ground state of certain types of classical spin models. Here, we show that arrays of metallic nanolasers provide an ultra-compact, on-chip platform capable of implementing spin models, including the classical Ising and XY Hamiltonians. Various regimes of behavior including ferromagnetic, antiferromagnetic, as well as geometric frustration are observed in these structures. Our work paves the way towards nanoscale spin-emulators that enable efficient modeling of large-scale complex networks.

中文翻译：

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基于纳米激光的自旋哈密顿模拟器

摘要 寻找一大类优化问题（称为 NP-hard 类）的解决方案需要使用传统计算机以指数方式增加求解时间。最近，人们一直在努力开发能够解决此类任务的替代计算方法。在这方面，自旋哈密顿量最初是在描述磁性材料中的交换相互作用时出现的，最近被用作强大的计算工具。沿着这些思路，已经表明，解决 NP 难题可以有效地映射到寻找某些类型的经典自旋模型的基态。在这里，我们展示了金属纳米激光器阵列提供了一个超紧凑的片上平台，能够实现自旋模型，包括经典的 Ising 和 XY Hamiltonian。在这些结构中观察到各种行为机制，包括铁磁、反铁磁以及几何挫折。我们的工作为实现大规模复杂网络的高效建模的纳米级自旋模拟器铺平了道路。