Quantum-enhanced learning with a controllable bosonic variational sensor network

TL;DR

This paper introduces a generalized quantum sensor network leveraging cavity-QED control for nonlinear data classification, demonstrating a threshold phenomenon where classification error sharply drops with increased probe energy.

Contribution

It extends the Gaussian-based SLAEN to handle nonlinear data by utilizing universal quantum control, providing a theoretical framework and analytical insights into error thresholds and noise effects.

Findings

Threshold phenomenon in classification error with probe energy

Significant error reduction above energy threshold

Implications for RF photonic sensors and dark matter detection

Abstract

The emergence of quantum sensor networks has presented opportunities for enhancing complex sensing tasks, while simultaneously introducing significant challenges in designing and analyzing quantum sensing protocols due to the intricate nature of entanglement and physical processes. Supervised learning assisted by an entangled sensor network (SLAEN) [Phys. Rev. X 9, 041023 (2019)] represents a promising paradigm for automating sensor-network design through variational quantum machine learning. However, the original SLAEN, constrained by the Gaussian nature of quantum circuits, is limited to learning linearly separable data. Leveraging the universal quantum control available in cavity-QED experiments, we propose a generalized SLAEN capable of handling nonlinear data classification tasks. We establish a theoretical framework for physical-layer data classification to underpin our approach. Through training quantum probes and measurements, we uncover a threshold phenomenon in classification error across various tasks -- when the energy of probes exceeds a certain threshold, the error drastically diminishes to zero, providing a significant improvement over the Gaussian SLAEN. Despite the non-Gaussian nature of the problem, we offer analytical insights into determining the threshold and residual error in the presence of noise. Our findings carry implications for radio-frequency photonic sensors and microwave dark matter haloscopes.