论文标题
具有变异纠缠传感器网络的量子增强数据分类
Quantum-enhanced data classification with a variational entangled sensor network
论文作者
论文摘要
基于嘈杂的中间量子量子（NISQ）硬件建立的变异量子电路（VQC），与经典处理结合使用，构成了量子模拟，经典优化和机器学习的有希望的体系结构。但是，所需的VQC深度以证明比经典方案具有量子优势，这超出了可用的NISQ设备的范围。由纠缠传感器网络（SLAE）协助的监督学习是一个独特的范式，它利用经典的机器学习算法训练的VQC，以量身定制传感器共享的多部分纠缠来解决实际有用的数据处理问题。在这里，我们报告了Slaen的第一个实验演示，并显示了纠缠较低的多维射频信号的误差概率。我们的工作为NISQ时代的量子增强数据处理及其应用铺平了一条新的途径。
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.
