Digital MemComputing machines (DMMs), which employ nonlinear dynamical systems with memory (time non-locality), have proven to be a robust and scalable unconventional computing approach for solving a wide variety of combinatorial optimization problems. However, most of the research so far has focused on the numerical simulations of the equations of motion of DMMs. This inevitably subjects time to discretization, which brings its own (numerical) issues that would be absent in actual physical systems operating in continuous time. Although hardware realizations of DMMs have been previously suggested, their implementation would require materials and devices that are not so easy to integrate with traditional electronics. In this study, we propose a novel hardware design for DMMs that leverages only conventional electronic components. Our findings suggest that this design offers a marked improvement in speed compared to existing realizations of these machines, without requiring special materials or novel device concepts. We also show that these DMMs are robust against additive noise. Moreover, the absence of numerical noise promises enhanced stability over extended periods of the machines' operation, paving the way for addressing even more complex problems.

翻译：数字MemComputing机器（DMMs）利用具有记忆（时间非局域性）的非线性动力学系统，已被证明是一种稳健且可扩展的非常规计算方法，可解决多种组合优化问题。然而，迄今为止大多数研究集中于DMMs运动方程的数值模拟。这不可避免地需要对时间进行离散化处理，从而引入实际连续时间物理系统中不存在的数值问题。尽管已有DMMs的硬件实现方案，但其实现需依赖难以与传统电子器件集成的材料和设备。本研究提出了一种仅采用常规电子元件的DMMs新型硬件设计。结果表明，与现有实现方案相比，该设计在速度上具有显著提升，且无需特殊材料或新型器件概念。我们还验证了这些DMMs对加性噪声具有鲁棒性。此外，数值噪声的消除有望在机器长时间运行中增强稳定性，为处理更复杂的问题铺平道路。