Quantum-enhanced physical-layer data learning with a variational sensor network

The emergence of quantum sensor networks has opened up the opportunity to enhance complex sensing tasks that routinely arise in practice. However, it has also brought tremendous challenges in designing and analyzing optimal quantum sensing protocols. Specifically, when it comes to hypothesis testing between physical-layer data modeled as quantum channels, the analytical approach is only sufficient to handle the simple linearly separable case. In this case, the error probability is reduced through Gaussian entanglement and measurement, as theoretically predicted in [Phys. Rev. X 9, 041023 (2019)] and experimentally verified in [Phys. Rev. X 11, 021047 (2021)]. This leaves an open question in the context of general nonlinear physical-layer data classification tasks. In this work, we develop supervised learning assisted by an entangled sensor network (SLAEN) for nonlinear classification. Empowered by universal quantum control readily available in cavity-QED experiments, we take a variational approach to train SLAEN to achieve the optimal advantage. In linearly separable tasks, we identify a threshold phenomenon in the classification error, where the error abruptly decreases to near-zero at a specific probe energy threshold. This results in a substantial advantage over classical or the previous Gaussian SLEAN. Despite the non-Gaussian nature of the problem, we provide analytical analyses to determine the threshold and residual error. In the case of nonlinear data, we also identify a significant advantage over classical or Gaussian strategies. Our findings have implications in the fields of radio-frequency photonic sensors and microwave dark matter haloscopes.

* NSF, DARPA, ONR.