Quantum-enhanced data classification with a variational entangled sensor network

TL;DR

This paper demonstrates a quantum-enhanced data classification method using a variational entangled sensor network, showing reduced error rates in classifying radio-frequency signals, and introduces a new approach for quantum data processing with NISQ devices.

Contribution

First experimental demonstration of SLAEN, leveraging VQCs and entanglement to improve data classification in practical NISQ hardware.

Findings

Reduced error probability in signal classification

Experimental validation of SLAEN's effectiveness

Paves new routes for quantum data processing

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.