QML for Quantum Sensing under Measurement-Induced Information Loss

2026-08-24

AI Summary

研究利用量子机器学习方法处理NV中心磁传感中的测量诱导信息丢失问题，通过比较经典与量子核模型，展示了量子态信息对提升传感性能的重要性。

Abstract

Nitrogen-vacancy (NV) centers in diamond can serve as highly sensitive solid-state quantum sensors for high-sensitivity magnetometry. However, in the noisy intermediate-scale quantum (NISQ) era, extracting reliable information from noisy, finite-shot, and measurement-limited sensing data remains a considerable challenge. Whereas, quantum machine learning (QML) offers a potential path to improve parameter estimation by learning nonlinear relationships between quantum-sensing data and the underlying physical signal. In this work, we investigate the role of QML in magnetic-field estimation within an NV center-inspired magnetometry setting. We formulated magnetic field sensing as a supervised regression task. We compared the performance of several classical machine learning models trained on measurement-based classical data with that of quantum kernel-based models trained on pre-measurement coherent quantum states. Our objective is to isolate the impact of measurement-induced information loss and therefore provide a theoretical upper bound on the sensing performance. The upper bound is achievable only when coherent quantum information is directly available to the learning model. Our results show that QML-based sensing performance improves significantly with coherent quantum-state information, and not much with changes in model complexity or learning paradigm. This observation underscores the importance of learning pipelines that tightly integrate quantum sensors and QML models to enhance magnetic field sensing under realistic constraints.

Problem

• Quantum Sensing
• Measurement-Induced Information Loss
• Nitrogen-Vacancy Centers
• Magnetic Field Estimation
• Quantum Machine Learning

Innovation

• Quantum Machine Learning
• Coherent Quantum States
• Magnetic Field Sensing
• Nitrogen-Vacancy Centers