Scientific Reports· 2026Q1
A two-stage optimization method for wide-range single-electron quantum magnetic sensing
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- 2026year
Short summary
A novel two-stage optimization protocol, combining a Bayesian neural network and federated reinforcement learning, significantly improves accuracy and efficiency for quantum magnetic sensing of wide-ranging signals, particularly in challenging NV-center spin systems.
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Key points
- A two-stage optimization protocol is proposed for quantum magnetic sensing.
- The protocol integrates a Bayesian neural network for signal range narrowing and federated reinforcement learning for calibration.
- It is evaluated on an NV-center spin system with single-shot readout and limited sensing time.
- The method achieves significant improvements in accuracy and resource efficiency compared to current state-of-the-art techniques.
AI-generated from the title and abstract; the full text is not read.
Abstract
Quantum magnetic sensing is a new paradigm for detecting ultra-weak magnetic fields. Its protocol design concerns the devise of optimal sensing parameters to estimate the underlying signals of interest (SoI). Existing studies 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, causing prolonged interrogation times and reduced accuracy. This work presents a new protocol using a two-stage optimization method, consisting of a Bayesian neural network to first narrow the SoI range and a federated reinforcement learning agent to subsequently calibrate the SoI. The protocol is developed and evaluated in the challenging context of an NV-center spin system with single-shot readout and limited sensing time; and yet it achieves significant improvements in both accuracy and resource efficiency compared to the state-of-the-art.
The authors' abstract, as published at the source. Scientific Reports, 2026 · DOI ↗
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Field: Materials Chemistry
Materials ChemistryMaterials Science