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Scientific Reports

Quantum magnetometry with superconducting-qubit Greenberger–Horne–Zeilinger and Dicke probes

Azat R. Gubaydullin, Vladimir Slepnev, Alexey Yu. Mironov, V. M. Vinokur

Published Aug 14, 2026• 0 citations
Publisher

Abstract

Quantum sensing has become a key technology for precision measurement, with applications spanning magnetometry, gravitational sensing, navigation and geodesy, materials and device diagnostics, and biomedical detection. This motivates the study of quantum-enhanced magnetometry with entangled states. We focus on Greenberger–Horne–Zeilinger (GHZ) and Dicke states operated within a Kitaev phase estimation algorithm (PEA). We consider superconducting fluxonium-qubit sensors, which are modelled with relaxation and dephasing noise. Using a Bayesian update procedure, we evaluate the estimation performance and compare the GHZ and Dicke sensors in terms of sensitivity, dynamical range, and information gain. GHZ probes achieve the highest local sensitivity, while Dicke probes maintain comparable sensitivity over a substantially wider bias interval and exhibit improved tolerance to bias errors and edge effects in PEA sequence. Our results demonstrate a trade-off between local peak sensitivity and the achievable dynamical range and robustness under noise.

DOI
10.1038/s41598-026-64752-w

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Quantum sensing has become a key technology for precision measurement, with applications spanning magnetometry, gravitational sensing, navigation and geodesy, materials and device diagnostics, and biomedical detection. This motivates the study of quantum-enhanced magnetometry with entangled states. We focus on Greenberger–Horne–Zeilinger (GHZ) and Dicke states operated within a Kitaev phase estimation algorithm (PEA). We consider superconducting fluxonium-qubit sensors, which are modelled with relaxation and dephasing noise. Using a Bayesian update procedure, we evaluate the estimation performance and compare the GHZ and Dicke sensors in terms of sensitivity, dynamical range, and information gain. GHZ probes achieve the highest local sensitivity, while Dicke probes maintain comparable sensitivity over a substantially wider bias interval and exhibit improved tolerance to bias errors and edge effects in PEA sequence. Our results demonstrate a trade-off between local peak sensitivity and the achievable dynamical range and robustness under noise.
Quantum metrology based on superconducting qubits offers unprecedented precision in measuring fundamental physical quantities, particularly magnetic fields. In this study, we develop a modified quantum phase estimation algorithm utilizing superconducting qubits, enhanced by optimized initial state preparation, significantly improving measurement sensitivity and reducing estimation times compared to conventional techniques. To ensure practical applicability and robustness, we incorporate advanced noise modeling, including intrinsic decoherence effects such as flux-induced noise and Purcell losses. Through detailed analytical modeling and numerical simulations, we systematically evaluate sensitivity and practical implementation constraints across various superconducting qubit architectures—transmon, fluxonium, conventional flux, and C-shunt flux qubits. Our approach quantifies the critical trade-offs between coherence, measurement dynamics, and sensitivity, identifying optimal operational regimes for high-performance quantum sensing. We introduce custom circuit simulation methods enabling the analysis and optimization of arbitrary superconducting quantum devices. Our findings provide a framework for advancing quantum-enhanced magnetometry and promise to facilitate next-generation quantum sensors for fundamental physics research, gravitational field measurements, and quantum-assisted searches for dark matter candidates.
Phase sensing with entangled multi-qubit states in the presence of noise is a central theme of modern quantum metrology. The present work investigates Dicke state superposition probes for quantum phase sensing under parameter encoding generated by one- and two-body interaction Hamiltonians. Under linear collective-spin encoding, near-optimal Dicke superposition states are shown to exhibit significantly enhanced robustness against phase damping noise compared with Greenberger--Horne--Zeilinger (GHZ), W-superposition, and balanced Dicke states, while maintaining favorable metrological performance under realistic decoherence channels. For two-body interactions, optimal probe states maximizing the quantum Fisher information are identified. Their noise resilience and metrological scaling behaviour under phase damping, amplitude damping, and global depolarizing channels are analyzed. The associated near-optimal Dicke superposition states are found to exhibit improved resilience to phase damping, for the system sizes considered. These results establish tailored near-optimal Dicke state superposition probes as versatile and noise-resilient resources for Heisenberg and super-Heisenberg quantum phase sensing governed by one- and two-body interactions.
Abstract Sensing and metrology are crucial in both fundamental science and practical applications. They meet the constant demand for precise data, enabling more dependable assessments of theoretical models’ validity. Sensors, now a common feature in many fields, play a vital role in applications like gravity imaging, geology, navigation, security, timekeeping, spectroscopy, chemistry, magnetometry, healthcare, and medicine. The advancements in quantum technologies have sparked interest in employing quantum systems as sensors, offering enhanced capabilities and new possibilities. This article describes the optimization of the quantum-enhanced sensing of magnetic fluxes with a Kitaev phase estimation algorithm based on frequency tunable transmon qubits. It provides the optimal flux biasing point for sensors with different qubit transition frequencies and gives an estimation of decoherence rates and achievable sensitivity. The use of 2- and 3-qubit entangled states are compared in simulation with the single-qubit case. The flux sensing accuracy reaches 10 − 8 ⋅ Φ 0 and scales inversely with time, which proves the speed-up of sensing with high ultimate accuracy.
Entanglement is a resource to improve the sensitivity of quantum sensors. In an ideal case, using an entangled state as a probe to detect target fields, we can beat the standard quantum limit by which all classical sensors are bounded. However, since entanglement is fragile against decoherence, it is unclear whether entanglement-enhanced metrology is useful in a noisy environment. Its benefit is indeed limited when estimating the amplitude of dc magnetic fields under the effect of parallel Markovian decoherence, where the noise operator is parallel to the target field. In this paper, on the contrary, we show an advantage to using an entanglement over the classical strategy under the effect of parallel Markovian decoherence when we try to detect ac magnetic fields. We consider a scenario to induce a Rabi oscillation of the qubits with the target ac magnetic fields. Although we can, in principle, estimate the amplitude of the ac magnetic fields from the Rabi oscillation, the signal becomes weak if the qubit frequency is significantly detuned from the frequency of the ac magnetic field. We show that, by using the Greenberger-Horne-Zeilinger (GHZ) states, we can significantly enhance the signal of the detuned Rabi oscillation even under the effect of parallel Markovian decoherence. Our method is based on the fact that the interaction time between the GHZ states and ac magnetic fields scales as 1/L1/L1/L to mitigate the decoherence effect, where LLL is the number of qubits, which contributes to improving the bandwidth of the detectable frequencies of the ac magnetic fields. Our results pave the way for new applications of entanglement-enhanced ac magnetometry.
We develop the quantum approach to magnetometry utilizing phase estimation algorithms, demonstrating improvements in the estimation of magnetic flux in both precision and dynamical range. We propose the modifications to conventional algorithms including the signal modulation and proximity time measurements. We demonstrate that our approach extends the dynamical range and improves the precision of magnetic flux detection. We show that our approach enhances performance of superconducting qubits and enables higher information gain without compromising dynamical range, paving the way toward achieving the Heisenberg limit. Combining adaptive algorithms with device-specific calibration, our methods bridge the gap between theoretical advancements and practical quantum sensing applications, offering a powerful framework for metrology using superconducting qubits.
Quantum magnetometry based on adaptive phase estimation allows for Heisenberg precision while avoiding creation and maintenance of complex entangled states. However, the absolute sensitivity is limited by the nonoptimal use of quantum resources provided by multiple-qubit devices and algorithmic realizations of the protocol. Here, addressing both issues, we advance the time-ascending phase estimation protocol by numerical improvements of Bayesian learning, i.e., sequential updating of the field distribution, and optimal exploitation of resources provided by unentangled qubits with limited coherence. Such algorithmic improvements are used to evaluate the absolute sensitivity both on a simulator and by pulsed-transmon experiments conducted on the IBMQ platform, where we take advantage of high coherence time. In addition, we compare the proficiency of separable and entangled states for magnetometry and show that, in practice, separable states provide superior performance. Flux-sensing emulation experiments demonstrate that a sensitivity of $(0.17--1.74)\ensuremath{\mu}{\mathrm{\ensuremath{\Phi}}}_{0}\phantom{\rule{4pt}{0ex}}{(\sqrt{\mathrm{Hz}})}^{\ensuremath{-}1}$ (where ${\mathrm{\ensuremath{\Phi}}}_{0}$ is the flux quantum) for a single-qubit magnetometer and $(0.06--0.65)\ensuremath{\mu}{\mathrm{\ensuremath{\Phi}}}_{0}\phantom{\rule{4pt}{0ex}}{(\sqrt{\mathrm{Hz}})}^{\ensuremath{-}1}$ for a five-qubit magnetometer can be achieved for slowly oscillating 1−−10kHz1--10\phantom{\rule{4pt}{0ex}}\mathrm{kHz}1−−10kHz magnetic fields, which is comparable to more established experimental platforms for magnetometry.