Recent advances in optical quantum computation set up a broad discussion on quantum supremacy and its practicability. Lack of programmability and extreme working conditions remain the challenges, calling for a programmable computation scheme. The quasi-2D layered materials introduce new architectures for the optical neural networks (ONNs), which support various programmable computations following the on-demand layer design. Compared with the traditional ONNs, Moiré ONNs architectures are more flexible to manufacture via layer number or twist angle control. A general Penn's model to demonstrate the mechanism inside is developed: the dielectric constant control through the layer and twisted bilayer angle dependence, respectively. Theoretically, this device can conduct demo computations ranging from boson sampling to image classification, where quantum computing shows its significant advantages. Instead of redundant 3D-printing and lithography in traditional ONNs, the Moiré computation framework can train different tasks through programmable twists on single layers without replacing materials.

中文翻译：

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走向可编程莫尔计算

光量子计算的最新进展引发了对量子霸权及其实用性的广泛讨论。缺乏可编程性和极端工作条件仍然是挑战，需要可编程计算方案。准 2D 分层材料为光神经网络 (ONN) 引入了新架构，该架构支持遵循按需层设计的各种可编程计算。与传统的 ONNs 相比，Moiré ONNs 架构通过层数或扭曲角控制更灵活地制造。开发了一个通用的 Penn 模型来演示内部机制：分别通过层和扭曲双层角度依赖性控制介电常数。理论上，该设备可以进行从玻色子采样到图像分类的演示计算，量子计算在其中显示了其显着优势。与传统 ONN 中冗余的 3D 打印和光刻不同，莫尔计算框架可以通过单层上的可编程扭曲来训练不同的任务，而无需更换材料。