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Related papers: Bayesian estimation in homodyne interferometry

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We address the problem of continuous-variable quantum phase estimation in the presence of linear disturbance at the Hamiltonian level, by means of Gaussian probe states. In particular we discuss both unitary and random disturbance, by…

Quantum Physics · Physics 2015-01-26 Douglas Delgado de Souza , Marco G. Genoni , M. S. Kim

Quantum parameter estimation offers solid conceptual grounds for the design of sensors enjoying quantum advantage. This is realised not only by means of hardware supporting and exploiting quantum properties, but data analysis has its impact…

This paper considers homography estimation in a Bayesian filtering framework using rate gyro and camera measurements. The use of rate gyro measurements facilitates a more reliable estimate of homography in the presence of occlusions, while…

Robotics · Computer Science 2023-10-17 Arturo Del Castillo Bernal , Philippe Decoste , James Richard Forbes

The theory of semiparametric estimation offers an elegant way of computing the Cram\'er-Rao bound for a parameter of interest in the midst of infinitely many nuisance parameters. Here I apply the theory to the problem of moment estimation…

Image and Video Processing · Electrical Eng. & Systems 2019-10-09 Mankei Tsang

We apply the theory of semiparametric estimation to a Hong-Ou-Mandel interference experiment with a spectrally entangled two-photon state generated by spontaneous parametric downconversion. Thanks to the semiparametric approach we can…

Quantum Physics · Physics 2022-04-21 Valeria Cimini , Francesco Albarelli , Ilaria Gianani , Marco Barbieri

We analyze the problem of quantum-limited estimation of a stochastically varying phase of a continuous beam (rather than a pulse) of the electromagnetic field. We consider both non-adaptive and adaptive measurements, and both dyne detection…

Quantum Physics · Physics 2007-05-23 D. W. Berry , H. M. Wiseman

The Cram\'er-Rao bound captures completely the performance of single-parameter quantum sensors. On the other hand, its extension to multiple parameters demands more caution. Different aspects need to be captured at once, including,…

Quantum Physics · Physics 2026-01-14 Jayanth Jayakumar , Marco Barbieri , Magdalena Stobińska

In this work, to improve the spin readout efficiency of the nitrogen vacancy (NV) center, a real-time Bayesian estimation algorithm is proposed, which combines both the prior probability distribution and the fluorescence likelihood function…

In this work we study the phase sensitivity of generic linear interferometric schemes using Gaussian resources and measurements. Our formalism is based on the Fisher information. This allows us to separate the contributions of the…

Quantum Physics · Physics 2021-03-25 Antonio A. Valido , Juan José García-Ripoll

We introduce a novel Bayesian phase estimation technique based on adaptive grid refinement method. This method automatically chooses the number particles needed for accurate phase estimation using grid refinement and cell merging strategies…

Quantum Physics · Physics 2020-09-18 Ramakrishna Tipireddy , Nathan Wiebe

For a fixed average energy, the simultaneous estimation of multiple phases can provide a better total precision than estimating them individually. We show this for a multimode interferometer with a phase in each mode, using Gaussian inputs…

Quantum Physics · Physics 2016-11-03 Christos N. Gagatsos , Dominic Branford , Animesh Datta

The estimation of the frequencies of multiple superimposed exponentials in noise is an important research problem due to its various applications from engineering to chemistry. In this paper, we propose an efficient and accurate algorithm…

Numerical Analysis · Mathematics 2016-05-05 Shanglin Ye , Elias Aboutanios

The Bayesian inversion method demonstrates significant potential for solving inverse problems, enabling both point estimation and uncertainty quantification (UQ). However, Bayesian maximum a posteriori (MAP) estimation may become unstable…

Numerical Analysis · Mathematics 2025-06-04 Ruibiao Song , Liying Zhang

We investigate the ultimate precision achievable in Gaussian quantum metrology. We derive general analytical expressions for the quantum Fisher information matrix and for the measurement compatibility condition, ensuring asymptotic…

Quantum Physics · Physics 2018-07-18 Rosanna Nichols , Pietro Liuzzo-Scorpo , Paul A. Knott , Gerardo Adesso

The phase of a single-mode field can be measured in a single-shot measurement by interfering the field with an effectively classical local oscillator of known phase. The standard technique is to have the local oscillator detuned from the…

Quantum Physics · Physics 2009-10-30 H. M. Wiseman

We derive an asymptotic lower bound on the Bayes risk when N identical quantum systems whose state depends on a vector of unknown parameters are jointly measured in an arbitrary way and the parameters of interest estimated on the basis of…

Statistics Theory · Mathematics 2023-05-02 Richard D. Gill

Phase diffusion represents a crucial obstacle towards the implementation of high precision interferometric measurements and phase shift based communication channels. Here we present a nearly optimal interferometric scheme based on homodyne…

We analyze the Heisenberg limit on phase estimation for Gaussian states. In the analysis, no reference to a phase operator is made. We prove that the squeezed vacuum state is the most sensitive for a given average photon number. We provide…

Quantum Physics · Physics 2007-05-23 Alex Monras

Many results in the quantum metrology literature use the Cram\'er-Rao bound and the Fisher information to compare different quantum estimation strategies. However, there are several assumptions that go into the construction of these tools,…

Quantum Physics · Physics 2018-01-31 Jesús Rubio , Paul Knott , Jacob Dunningham

Jump diffusion processes are widely used to model asset prices over time, mainly for their ability to capture complex discontinuous behavior, but inference on the model parameters remains a challenge. Here our goal is posterior inference on…

Methodology · Statistics 2017-02-23 Ryan Martin , Cheng Ouyang , Francois Domagni