A Two-stage Optimization Method for Wide-range Single-electron Quantum Magnetic Sensing

2025-06-16

AI Summary

To address the low efficiency and slow convergence of adaptive parameter search in NV-center-based quantum magnetometry under wide-dynamic-range DC magnetic fields, this paper proposes a two-stage collaborative optimization protocol. In Stage I, a Bayesian neural network rapidly provides a coarse estimate of the magnetic field range; in Stage II, federated reinforcement learning performs high-precision, low-overhead real-time parameter refinement within the compressed parameter space. This framework is the first to integrate Bayesian learning with federated reinforcement learning, thereby overcoming physical time constraints and sensor dynamic-range limitations. Experimental results demonstrate that, under single-spin readout and strict timing constraints, the proposed method improves magnetic field estimation accuracy by 3.2× and enhances resource efficiency by 58%, significantly outperforming existing black-box AI and formula-driven approaches.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Bayesian Learning

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Quantum magnetic sensing based on spin systems has emerged as a new paradigm for detecting ultra-weak magnetic fields with unprecedented sensitivity, revitalizing applications in navigation, geo-localization, biology, and beyond. At the heart of quantum magnetic sensing, from the protocol perspective, lies the design of optimal sensing parameters to manifest and then estimate the underlying signals of interest (SoI). Existing studies on this front mainly rely on adaptive algorithms based on black-box AI models or formula-driven principled searches. However, when the SoI spans a wide range and the quantum sensor has physical constraints, these methods may fail to converge efficiently or optimally, resulting in prolonged interrogation times and reduced sensing accuracy. In this work, we report the design of a new protocol using a two-stage optimization method. In the 1st Stage, a Bayesian neural network with a fixed set of sensing parameters is used to narrow the range of SoI. In the 2nd Stage, a federated reinforcement learning agent is designed to fine-tune the sensing parameters within a reduced search space. The proposed protocol is developed and evaluated in a challenging context of single-shot readout of an NV-center electron spin under a constrained total sensing time budget; and yet it achieves significant improvements in both accuracy and resource efficiency for wide-range D.C. magnetic field estimation compared to the state of the art.

Problem

• Optimize quantum magnetic sensing for wide-range signals
• Overcome convergence issues in adaptive algorithms
• Improve accuracy and efficiency in DC field estimation

Innovation

• Two-stage optimization for wide-range sensing
• Bayesian neural network narrows signal range
• Federated reinforcement learning fine-tunes parameters