可重构耦合振子平台求解最大割问题的实验验证
Experimental Demonstration of a Reconfigurable Coupled Oscillator Platform to Solve the Max-Cut Problem
摘要：
在这项工作中，我们通过实验证明了30个弛张振荡器的集成电路（IC）与可重构的电容耦合，以解决NP难最大切割（最大切割）问题。我们表明，在外部二次谐波注入信号的影响下，振荡器的相位表现出一个双分区，可以用来计算一个高质量的近似Max-Cut解决方案。利用所有到所有的可重构耦合架构，我们实验评估的振荡器使用随机生成的图形实例的不同大小和边缘密度的计算特性。此外，最大切割的解决方案与最佳值进行比较，我们表明，振荡器（经过简单的后处理）产生的最大切割是在99%的最佳值在28的36个测量图;重要的是，振荡器是特别有效的密集的图形与最大切割是最佳的在七个测得的图与边缘密度0.8。我们的工作标志着朝着创建一个高效的，室温兼容的非布尔硬件为基础的求解器硬组合优化问题的一步。
英文摘要：
In this work, we experimentally demonstrate an integrated circuit (IC) of 30 relaxation oscillators with reconfigurable capacitive coupling to solve the NP-Hard Maximum Cut (Max-Cut) problem. We show that under the influence of an external second-harmonic injection signal, the oscillator phases exhibit a bi-partition which can be used to calculate a high quality approximate Max-Cut solution. Leveraging the all-to-all reconfigurable coupling architecture, we experimentally evaluate the computational properties of the oscillators using randomly generated graph instances of varying size and edge density . Further, comparing the Max-Cut solutions with the optimal values, we show that the oscillators (after simple post-processing) produce a Max-Cut that is within 99% of the optimal value in 28 of the 36 measured graphs; importantly, the oscillators are particularly effective in dense graphs with the Max-Cut being optimal in seven out of nine measured graphs with edge density 0.8. Our work marks a step towards creating an efficient, room-temperature-compatible non-Boolean hardware-based solver for hard combinatorial optimization problems.
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