Variational quantum circuits (VQCs) built upon noisy intermediate-scale quantum (NISQ) hardware, in conjunction with classical processing, constitute a promising architecture for quantum simulations, classical optimization, and machine learning. However, the required VQC depth to demonstrate a quantum advantage over classical schemes is beyond the reach of available NISQ devices. Supervised learning assisted by an entangled sensor network (SLAEN) is a distinct paradigm that harnesses VQCs trained by classical machine-learning algorithms to tailor multipartite entanglement shared by sensors for solving practically useful data-processing problems. Here, we report the first experimental demonstration of SLAEN and show an entanglement-enabled reduction in the error probability for classification of multidimensional radio-frequency signals. Our work paves a new route for quantum-enhanced data processing and its applications in the NISQ era.

中文翻译：

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具有变分纠缠传感器网络的量子增强数据分类

建立在嘈杂的中尺度量子 (NISQ) 硬件上的变分量子电路 (VQC) 与经典处理相结合，构成了用于量子模拟、经典优化和机器学习的有前途的架构。然而，证明优于经典方案的量子优势所需的 VQC 深度超出了现有 NISQ 设备的能力范围。纠缠传感器网络 (SLAEN) 辅助的监督学习是一种独特的范式，它利用由经典机器学习算法训练的 VQC 来定制传感器共享的多部分纠缠，以解决实际有用的数据处理问题。在这里，我们报告了 SLAEN 的首次实验演示，并展示了纠缠使多维射频信号分类的错误概率降低。