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) (Zhuang and Zhang 2019 Phys. Rev. X 9 041023) 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 quantum electrodynamics 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.

中文翻译：

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利用可控玻色变分传感器网络进行量子增强学习


量子传感器网络的出现为增强复杂传感任务提供了机会，同时由于纠缠和物理过程的复杂性，给设计和分析量子传感协议带来了重大挑战。纠缠传感器网络 (SLAEN) 辅助的监督学习（Zhang 和 Zhang 2019 Phys. Rev. X 9 041023）代表了通过变分量子机器学习实现传感器网络设计自动化的有前途的范例。然而，原始的SLAEN受到量子电路高斯性质的限制，仅限于学习线性可分离的数据。利用腔量子电动力学实验中可用的通用量子控制，我们提出了一种能够处理非线性数据分类任务的广义 SLAEN。我们建立了物理层数据分类的理论框架来支撑我们的方法。通过训练量子探针和测量，我们发现了各种任务中分类错误的阈值现象——当探针的能量超过某个阈值时，错误会急剧减小到零，从而比高斯 SLAEN 有了显着的改进。尽管问题具有非高斯性质，但我们提供了在存在噪声的情况下确定阈值和残差的分析见解。我们的研究结果对射频光子传感器和微波暗物质光晕镜具有重要意义。