Quantum-Enhanced Data Classification with a Variational Entangled Sensor Network

Yi Xia, Wei Li, Quntao Zhuang, Zheshen Zhang

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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.

Original languageEnglish (US)
Article number021047
JournalPhysical Review X
Issue number2
StatePublished - Jun 2021

ASJC Scopus subject areas

  • Physics and Astronomy(all)


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