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.
Highlights
The convergence of quantum information science and machine learning (ML) has endowed radically new capabilities for solving complex physical and data-processing problems [1,2,3,4,5,6,7,8,9]
SLAEN first copes with 2D data acquired by two entangled sensors, as illustrated in data classification, we demonstrate the classification of the incident direction of an emulated rf field
Our work opens a new route for exploiting noisy intermediate-scale quantum (NISQ) hardware to enhance the performance of real-world data-processing tasks
Summary
The convergence of quantum information science and machine learning (ML) has endowed radically new capabilities for solving complex physical and data-processing problems [1,2,3,4,5,6,7,8,9]. Recent developments in hybrid systems [9,12] comprising classical processing and variational quantum circuits (VQCs) open an alternative avenue for quantum ML. In this regard, a variety of hybrid schemes have been proposed, including quantum approximate optimization [13], variational quantum eigensolvers [14], quantum multiparameter estimation [15], and quantum kernel estimators and variational quantum models [4,5]. Hybrid schemes have been implemented to seek the ground state of quantum systems [14,16], to perform data classification [4], to unsample a quantum circuit [17], and to solve the MAXCUT problem [18,19]. An imperative objective for quantum ML is to harness NISQ hardware to benefit practically useful applications [2]
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