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For a given quantum state used in sensing, the quantum Cram\'er-Rao bound (QCRB) sets a fundamental limit on the precision achievable by an unbiased estimator of an unknown parameter, determined by the inverse of the quantum Fisher…

Quantum Physics · Physics 2026-01-14 Andrew Kolmer Forbes , Marco A. Rodríguez-García , Ivan H. Deutsch

We perform an analysis supplementing the metrology toolbox in the time-frequency domain. While the relevant time-frequency-based metrological protocols can be borrowed from the spatial domain, where they have recently been well developed,…

Quantum Physics · Physics 2024-07-30 Łukasz Rudnicki , Tomasz Linowski

A version of quantum Cram\'{e}r-Rao bound dictates that the covariance of any set of operators is bounded by a product of the derivatives of expectation values and the inverse of quantum metric. We elaborate that because quantum metric…

Quantum Physics · Physics 2026-03-06 Wei Chen

The quantum Cram\'er-Rao bound sets a fundamental limit on the accuracy of unbiased parameter estimation in quantum systems, relating the uncertainty in determining a parameter to the inverse of the quantum Fisher information. We…

Neural networks are increasingly used to estimate parameters in quantitative MRI, in particular in magnetic resonance fingerprinting. Their advantages over the gold standard non-linear least square fitting are their superior speed and their…

A central challenge in quantum metrology is identifying optimal measurements that saturate the quantum Cramer-Rao bound under realistic constraints, e.g., local measurements. We show that symmetries of the probe state provide a general…

Quantum Physics · Physics 2026-03-10 Jia-Xuan Liu , Hai-Long Shi , Chunfeng Wu , Sixia Yu

Quantum metrology protocols are typically designed around the assumption that we have an abundance of measurement data, but recent practical applications are increasingly driving interest in cases with very limited data. In this regime the…

Quantum Physics · Physics 2019-04-18 Jesús Rubio , Jacob Dunningham

Bounding the optimal precision in parameter estimation tasks is of central importance for technological applications. In the regime of a small number of measurements, or that of low signal-to-noise ratios, the meaning of common frequentist…

Quantum Physics · Physics 2024-02-23 Valentin Gebhart , Manuel Gessner , Augusto Smerzi

The quantum Cram\'er-Rao bound (QCRB) sets a fundamental limit for the measurement of classical signals with detectors operating in the quantum regime. Using linear-response theory and the Heisenberg uncertainty relation, we derive a…

Quantum Physics · Physics 2017-08-09 Haixing Miao , Rana X Adhikari , Yiqiu Ma , Belinda Pang , Yanbei Chen

Critical metrology relies on the precise preparation of a system in its ground state near a quantum phase transition point where quantum correlations get very strong. Typically this increases the quantum Fisher information with respect to…

Quantum Physics · Physics 2024-02-12 Christoph Hotter , Helmut Ritsch , Karol Gietka

Quantum measurement plays a crucial role in quantum metrology. Due to the limitations of experimental capabilities, collectively measuring multiple copies of probing systems can present significant challenges. Therefore, the concept of…

Quantum Physics · Physics 2025-02-24 Jia-Xuan Liu , Jing Yang , Hai-Long Shi , Sixia Yu

Measurement incompatibility is a cornerstone of quantum mechanics. In the context of estimating multiple parameters of a quantum system, this manifests as a fundamental trade-off between the precisions with which different parameters can be…

Quantum Physics · Physics 2025-11-11 Simon K. Yung , Aritra Das , Jun Suzuki , Ping Koy Lam , Jie Zhao , Lorcán O. Conlon , Syed M. Assad

The Fisher Information Metric (FIM) and the associated Cram\'er-Rao Bound (CRB) are fundamental tools in statistical signal processing, which inform the efficient design of experiments and algorithms for estimating the underlying…

Signal Processing · Electrical Eng. & Systems 2025-05-20 Shiraz Khan , Gregory S. Chirikjian

One-bit quantization has garnered significant attention in recent years for various signal processing and communication applications. Estimating model parameters from one bit quantized data can be challenging, particularly when the…

Signal Processing · Electrical Eng. & Systems 2025-04-01 Nadav E. Rosenthal , Joseph Tabrikian

The quantum Cram\'er-Rao theorem states that the quantum Fisher information (QFI) bounds the best achievable precision in the estimation of a quantum parameter $\xi$. This is true, however, under the assumption that the measurement employed…

Quantum Physics · Physics 2018-09-26 Luigi Seveso , Matteo G. A. Paris

We investigate the problem of estimating simultaneously multiple parameters encoded in the shape of the modes on which the light is expanded. For this, we generalize the mode-encoded parameter estimation theory as introduced in Ref.[1] to a…

Quantum Physics · Physics 2025-05-23 Alexander Boeschoten , Giacomo Sorelli , Manuel Gessner , Claude Fabre , Nicolas Treps

Quantum metrology studies quantum strategies which enable us to outperform their classical counterparts. In this framework, the existence of perfect classical reference frames is usually assumed. However, such ideal reference frames might…

Quantum Physics · Physics 2015-03-17 Dominik Šafránek , Mehdi Ahmadi , Ivette Fuentes

We introduce quantum parameter estimation with the encoding being via a quantum measurement. We quantify the precision for estimating parameters characterizing a general two-outcome qubit measurement, considering two cases: when the…

Quantum Physics · Physics 2025-12-12 Shuva Mondal , Priya Ghosh , Ujjwal Sen

The Cram\'er-Rao bound serves as a crucial lower limit for the mean squared error of an estimator in frequentist parameter estimation. Paradoxically, it requires highly accurate prior knowledge of the estimated parameter for constructing…

Quantum Physics · Physics 2025-04-21 Javier Navarro , Ricard Ravell Rodríguez , Mikel Sanz

The Bayesian Cram\'er-Rao bound (CRB) provides a lower bound on the mean square error of any Bayesian estimator under mild regularity conditions. It can be used to benchmark the performance of statistical estimators, and provides a…

Machine Learning · Statistics 2024-09-09 Evan Scope Crafts , Xianyang Zhang , Bo Zhao