量子随机网络中子图提取的光子实现
Photonic realization of a subgraph extraction in a quantum random network
AI总结：
本研究采用集成硅光子芯片，在四节点量子随机网络中实验实现了量子随机网络理论预测的量子子图，验证了该子图态的高维多体纠缠，证明量子纠缠可实现经典无法企及的连接结构。
AI中文摘要：
理解复杂连接性如何在网络中产生是经典与量子科学领域的基础挑战。在经典随机网络中，复杂子图通常需要相对较高的连接概率，而量子随机网络理论预测，这类结构可通过纠缠与局域操作在单一更低阈值处产生。本研究采用集成硅光子芯片，在四节点量子随机网络中实验实现了量子随机网络理论预测的量子子图。该集成平台利用概率性光子对源与路径模式的相干控制，通过局域变换与后选择制备结构化量子子图，其工作阈值与经典随机网络不同。研究验证该子图态在节点间呈现真实的高维多体纠缠，为量子纠缠可实现经典无法企及的连接结构提供了实验证据。
英文摘要：
Understanding how complex connectivity emerges in networks is a fundamental challenge in classical and quantum science. In classical random networks, complex subgraphs typically require relatively high connection probabilities, whereas quantum random network theory predicts that such structures can arise at a single, lower threshold through entanglement and local operations. Here, using an integrated silicon photonic chip, we experimentally realize a quantum subgraph predicted by quantum random network theory in a four-node quantum random network. Our integrated platform exploits probabilistic photon-pair sources and coherent control of path modes to prepare a structured quantum subgraph through local transformations and postselection, operating in a threshold regime that differs from classical random networks. We verify that the subgraph state exhibits genuine high-dimensional multipartite entanglement across the nodes, providing experimental evidence that quantum entanglement enables connectivity structures beyond classical accessibility.
