Quantum-enhanced data classification with a variational entangled sensor network

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 overclassical schemes is beyond the reach of available NISQ devices. Supervised learning assisted by an entangledsensor network (SLAEN) is a distinct paradigm that harnesses VQCs trained by classical machine learning algorithms to tailor multipartite entanglement shared by the 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.