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arXiv 2404.18293quant-ph
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具有可控玻色变分传感器网络的量子增强学习

Quantum-enhanced learning with a controllable bosonic variational sensor network

Pengcheng Liao, Bingzhi Zhang, Quntao Zhuang

AI总结:

针对原始SLAEN仅能处理线性可分数据的局限,提出基于cavity-QED的广义SLAEN框架处理非线性分类,发现探针能量超阈值时误差降至零,并给出噪声下的分析见解。

摘要:

量子传感器网络的出现为增强复杂传感任务带来了机遇,但同时也因纠缠和物理过程的复杂性,在设计和分析量子传感协议方面引入了重大挑战。由纠缠传感器网络辅助的监督学习(SLAEN)[Phys. Rev. X 9, 041023 (2019)]代表了一种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式。然而,受限于量子电路的高斯特性,原始的SLAEN仅限于学习线性可分数据。利用cavity-QED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。我们建立了一个物理层数据分类的理论框架来支撑我们的方法。通过对量子探针和测量的训练,我们揭示了各种任务中分类误差的阈值现象——当探针的能量超过特定阈值时,误差急剧减小至零,相较于高斯SLAEN提供了显著改进。尽管问题具有非高斯性质,我们仍提供了在噪声存在下确定阈值和残余误差的分析见解。我们的发现对射频光子传感器和微波暗物质haloscopes具有重要意义。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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