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arXiv · 2006.11962

Quantum-enhanced data classification with a variational entangled sensor network

Yi Xia·Wei Li·Quntao Zhuang·Zheshen Zhang
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Abstract

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.

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Yi Xia, Wei Li, Quntao Zhuang, Zheshen Zhang. 2021-05-18. Quantum-enhanced data classification with a variational entangled sensor network. https://doi.org/10.1103/physrevx.11.021047

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