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Research PaperResearchia:202609.21079

Intrinsic Vectorial Gradiometry via Quantum Control of a Spin-based Sensor

Jaime García Oliván

Abstract

Gradiometry provides a versatile alternative to passive environmental shielding in quasi-static magnetometry, effectively suppressing background noise through differential signal extraction. Nevertheless, traditional implementations rely on multi-sensor architectures restricted to spatial gradients, where subtracting signals from independent detectors involves imperfect suppression of common-mode noise and artifacts, limiting their sensitivity. To overcome these limitations, we introduce a quant...

Submitted: September 21, 2026Subjects: Quantum Physics; Quantum Computing

Description / Details

Gradiometry provides a versatile alternative to passive environmental shielding in quasi-static magnetometry, effectively suppressing background noise through differential signal extraction. Nevertheless, traditional implementations rely on multi-sensor architectures restricted to spatial gradients, where subtracting signals from independent detectors involves imperfect suppression of common-mode noise and artifacts, limiting their sensitivity. To overcome these limitations, we introduce a quantum control sequence that enables intrinsic temporal and spatial vectorial gradiometry of magnetic fields using a single quantum sensor. Our method provides direct access to first and higher-order derivatives of the magnetic field and extended applicability via auxiliary nuclear spin memory. We showcase this protocol on an ensemble of nitrogen-vacancy (NV) centers in diamond and combine it with mechanical control to realize high-precision differential sensing. Through detailed numerical simulations, we demonstrate the performance of our scheme in two critical DC magnetometry applications: (i) vector magnetic anomaly detection and (ii) non-invasive gradiometry of neuronal action potentials.


Source: arXiv:2609.21776v1 - http://arxiv.org/abs/2609.21776v1 PDF: https://arxiv.org/pdf/2609.21776v1 Original Link: http://arxiv.org/abs/2609.21776v1

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Date:
Sep 21, 2026
Topic:
Quantum Computing
Area:
Quantum Physics
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