论文标题
通过选择非线性诱导的模拟式机器的计算性能差异差异
Order-of-magnitude differences in computational performance of analog Ising machines induced by the choice of nonlinearity
论文作者
论文摘要
基于非线性模拟系统的Ising机器是加速NP-HARD优化问题的有前途的方法。然而，它们的模拟性质也导致幅度不均匀性，这可能会恶化找到最佳解决方案的能力。在这里，我们研究了系统的非线性传输功能如何减轻振幅不均匀性并改善计算性能。通过模拟具有多项式，周期性，sigmoid和剪切的传输功能并使用MaxCut优化问题对其进行基准测试，我们发现传递函数的选择对计算时间和解决方案质量有重大影响。对于周期性，乙状结肠和剪辑转移函数，我们报告了与常规多项式模型相比，时间到解决方案的提高顺序改善，我们将其与抑制振幅不均匀性抑制均与传递函数饱和引起。这提供了有关系统用于构建Ising机器的适用性的见解，并为克服性能限制提供了有效的方法。
Ising machines based on nonlinear analog systems are a promising method to accelerate computation of NP-hard optimization problems. Yet, their analog nature is also causing amplitude inhomogeneity which can deteriorate the ability to find optimal solutions. Here, we investigate how the system's nonlinear transfer function can mitigate amplitude inhomogeneity and improve computational performance. By simulating Ising machines with polynomial, periodic, sigmoid and clipped transfer functions and benchmarking them with MaxCut optimization problems, we find the choice of transfer function to have a significant influence on the calculation time and solution quality. For periodic, sigmoid and clipped transfer functions, we report order-of-magnitude improvements in the time-to-solution compared to conventional polynomial models, which we link to the suppression of amplitude inhomogeneity induced by saturation of the transfer function. This provides insights into the suitability of systems for building Ising machines and presents an efficient way for overcoming performance limitations.
