论文标题
多维超刺机
Multidimensional hyperspin machine
论文作者
论文摘要
从冷凝物到量子染色体动力学，多维旋转是一种基本范式，在组合优化和机器学习中具有关键作用。由耦合参数振荡器形成的机器可以模拟自旋模型，但仅用于ISING或低维旋转。当前，实施任意维度的机器仍然是一个挑战。在这里，我们介绍并验证一台超刺机以模拟多维连续自旋模型。我们通过泵送参数振荡器的组和研究超刺蛋白的NP-硬态图实现了高维的旋转。 Hyperspin机器可以通过调整耦合拓扑来插值不同的维度，这是我们称为“维度退火”的策略。当XY和Ising模型之间进行插值时，与常规ISINS模拟器相比，尺寸退火会增加成功概率。 Hyperspin机器是组合优化的新计算模型。可以通过现成的硬件来实现它们，以用于超快，大规模应用，用于古典和量子计算，凝结物理学和基本研究。
From condensed matter to quantum chromodynamics, multidimensional spins are a fundamental paradigm, with a pivotal role in combinatorial optimization and machine learning. Machines formed by coupled parametric oscillators can simulate spin models, but only for Ising or low-dimensional spins. Currently, machines implementing arbitrary dimensions remain a challenge. Here, we introduce and validate a hyperspin machine to simulate multidimensional continuous spin models. We realize high-dimensional spins by pumping groups of parametric oscillators, and study NP-hard graphs of hyperspins. The hyperspin machine can interpolate between different dimensions by tuning the coupling topology, a strategy that we call "dimensional annealing". When interpolating between the XY and the Ising model, the dimensional annealing impressively increases the success probability compared to conventional Ising simulators. Hyperspin machines are a new computational model for combinatorial optimization. They can be realized by off-the-shelf hardware for ultrafast, large-scale applications in classical and quantum computing, condensed-matter physics, and fundamental studies.
