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 otherwise 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. Addressing this, our study introduces a novel hardware design for DMMs, utilizing readily available electronic components. This approach not only significantly boosts computational speed compared to current models but also exhibits remarkable robustness against additive noise. Crucially, it circumvents the limitations imposed by numerical noise, ensuring enhanced stability and reliability during extended operations. This paves a new path for tackling increasingly complex problems, leveraging the inherent advantages of DMMs in a more practical and accessible framework.

翻译：数字记忆计算机器（DMMs）采用具有记忆（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传统计算方法，适用于解决各类组合优化问题。然而，迄今为止的研究大多聚焦于DMM运动方程的数值模拟。这不可避免地导致时间离散化，从而引发实际连续时间物理系统中本不存在的（数值）问题。尽管先前已有DMM硬件实现的提议，但其实现需要难以与传统电子器件集成的材料和设备。针对这一问题，本研究提出了一种利用现成电子元件的新型DMM硬件设计。该方法不仅较现有模型显著提升了计算速度，还表现出对加性噪声的卓越鲁棒性。更重要的是，它规避了数值噪声带来的限制，确保了长时间运行下的增强稳定性与可靠性。这为利用DMM固有优势解决日益复杂的问题，在更实用、更易实现的框架内开辟了新路径。