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Results indicate enhanced performance for superconducting qubits and higher information gain without sacrificing dynamical range, supporting progress toward the Heisenberg limit. Adaptive algorithms combined with device-specific calibration connect theoretical advances to practical quantum sensing.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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the main goal of the proposed phase estimation approach for magnetometry?","Question",{"text":62,"@type":63},"The approach aims to improve magnetic-flux estimation by increasing both precision and dynamical range using quantum phase estimation algorithms with artificial atoms.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"Which modifications are introduced to conventional phase estimation algorithms?",{"text":67,"@type":63},"The work proposes changes including signal modulation and proximity time measurements, designed to extend dynamical range while improving flux detection.",{"name":69,"@type":60,"acceptedAnswer":70},"How does the method connect quantum sensors to practical device performance?",{"text":71,"@type":63},"It combines adaptive phase estimation algorithms with device-specific calibration, bridging theoretical advances and practical quantum sensing applications using superconducting 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enhanced magnetometry with artificial atoms  \nVladimir Slepnev, Azat Gubaydullin􀀍 & ValeriiVinokur􀀍  \nWe 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  \nwith device-specific calibration, our methods bridge the gap between theoretical advancementsand practical quantum sensing applications, offering a powerful framework for metrology using superconducting qubits.  \nKeywords Phase estimation algorithms, Qubits, Sensing, Quantum magnetometry  \nQuantum sensing and metrology represent powerful frameworks for achieving precise measurements of physical quantities by exploiting the inherent quantum properties of probe systems 1,2. These quantum sensors enable measuring a broad spectrum of phenomena, including gravitational forces, electromagnetic fields, and photon propagation, with unprecedented precision that surpasses the fundamental limits of classical techniques. Quantum sensors find applications in diverse fields, ranging from fundamental physics 3–5 to medical imaging 6,7, industrial diagnostics 8,9, and advanced technological applications 10, establishing their indispensable role across various domains.  \nMany leading platforms for quantum sensing including the nitrogen-vacancy (NV) centers in diamond 11, cold-atom magnetometry 12, dc-superconducting quantum interference devices (SQUIDs) 13, 14 and superconducting qubits 15, 16 attract significant attention. Superconducting qubits, in particular, offer exceptional opportunities for enhanced sensitivity in detecting weak magnetic fields and other environmental perturbations, primarily due to their ability to exploit quantum coherence and entanglement. Transmon qubits 15 are applied in magnetometry 16–18 due to their flux tunability and promising relaxation times. In this work, we explore fluxonium qubits 19–21 for quantum sensing, which offer longer coherence T1 and dephasing T2 times, higher inductance, and reduced susceptibility to charge noise.  \nPhase estimation algorithms (PEAs) are an integral part of quantum sensing protocols 22,23, serving as key tools for enhancing measurement precision. These algorithms 24–27, developed originally for quantum computing tasks such as Shor’s factorization algorithm 28,29 and Lloyd’s algorithm for solving linear systems 30, have become equally vital in quantum metrology 31–43. By enabling the precise estimation of unknown parameters that influence the quantum sensor’s energy spectrum, PEAs facilitate enhanced sensing. Optimizing the design of chip architecture incorporating superconducting qubits 17,21 and employing advanced PEAs promises to significantly improve the magnetic flux sensitivity, pushing up the limits of quantum-enhanced sensing.  \nMagnetometry with superconducting qubits  \nQuantum magnetometry using superconducting qubits leverages the sensitivity of the qubit’s transition frequency to external magnetic flux. A qubit device 44, often described as an artificial atom 16, carries the fundamental unit of quantum information, analogous to the classical bit. However, a qubit is a quantum two-level system that can exist not only in its two basic states, its ground state |0⟩ and excited state |1⟩, but, also in a superposition of these states. The possibility of such a superposition enables qubits45–47 to outperform the","cbCaippwJtrHMC7W","https://ap.wps.com/l/cbCaippwJtrHMC7W","pdf",4823874,"English","# Introduction\n## Quantum sensing and metrology\n## Superconducting qubits and artificial atoms\n## Phase estimation algorithms in quantum metrology\n# Quantum magnetometry with superconducting qubits\n## Qubit as an artificial atom and flux-dependent transition frequency\n## Passport function and flux inference\n## Ramsey sequence for phase accumulation\n## Coherence and dephasing times","[{\"question\":\"What is the main goal of the proposed phase estimation approach for magnetometry?\",\"answer\":\"The approach aims to improve magnetic-flux estimation by increasing both precision and dynamical range using quantum phase estimation algorithms with artificial atoms.\"},{\"question\":\"Which modifications are introduced to conventional phase estimation algorithms?\",\"answer\":\"The work proposes changes including signal modulation and proximity time measurements, designed to extend dynamical range while improving flux detection.\"},{\"question\":\"How does the method connect quantum sensors to practical device performance?\",\"answer\":\"It combines adaptive phase estimation algorithms with device-specific calibration, bridging theoretical advances and practical quantum sensing applications using superconducting qubits.\"}]","Phase estimation algorithms for quantum enhanced magnetometry with artificial atoms | PDF",1790733241,48]