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All the $n(2n+3)$ mean and covariance parameters of an $n$-mode Gaussian states are expressed in terms of the expectation values of the same number of conjugates of the total number observable. This permits a complete tomography of the…

Quantum Physics · Physics 2016-01-14 K. R. Parthasarathy , Ritabrata Sengupta

Though Gaussian graphical models have been widely used in many scientific fields, relatively limited progress has been made to link graph structures to external covariates. We propose a Gaussian graphical regression model, which regresses…

Methodology · Statistics 2022-02-01 Jingfei Zhang , Yi Li

In this work, we consider the estimation of single mode Gaussian states using four different measurement schemes namely: i) homodyne measurement, ii) sequential measurement, iii) Arthurs-Kelly scheme, and iv) heterodyne measurement, with a…

Quantum Physics · Physics 2022-04-18 Chandan Kumar , Arvind

In this paper, the problem of state estimation, in the context of both filtering and smoothing, for nonlinear state-space models is considered. Due to the nonlinear nature of the models, the state estimation problem is generally intractable…

Machine Learning · Statistics 2021-11-24 Jarrad Courts , Adrian Wills , Thomas B. Schön

Quantum state tomography is a technique in quantum information science used to reconstruct the density matrix of an unknown quantum state, providing complete information about the quantum state. It is of significant importance in fields…

Quantum Physics · Physics 2025-07-23 Wenlong Zhao , Da Zhang , Huili Zhang , Haifeng Yu , Zhang-qi Yin

Continuous-variable Gaussian entanglement is an attractive notion, both as a fundamental concept in quantum information theory, based on the well-established Gaussian formalism for phase-space variables, and as a practical resource in…

Quantum Physics · Physics 2026-05-07 E. Shchukin , P. van Loock

Reconstructing quantum states is an important task for various emerging quantum technologies. The process of reconstructing the density matrix of a quantum state is known as quantum state tomography. Conventionally, tomography of arbitrary…

Quantum Physics · Physics 2020-08-17 Sanjib Ghosh , Andrzej Opala , Michał Matuszewski , Tomasz Paterek , Timothy C. H. Liew

Gaussian process is a theoretically appealing model for nonparametric analysis, but its computational cumbersomeness hinders its use in large scale and the existing reduced-rank solutions are usually heuristic. In this work, we propose a…

Machine Learning · Statistics 2015-11-25 Leo L. Duan , Xia Wang , Rhonda D. Szczesniak

In some cases the state of a quantum system with a large number of subsystems can be approximated efficiently by the density matrix renormalization group, which makes use of redundancies in the description of the state. Here we show that…

Strongly Correlated Electrons · Physics 2009-02-03 Michael J. Hartmann , Javier Prior , Stephen R. Clark , Martin B. Plenio

Coherent states provide an appealing method to reconstruct efficiently a pure state of a quantum mechanical spin s. A Stern-Gerlach apparatus is used to measure (4s+1) expectations of projection operators on appropriate coherent states in…

Quantum Physics · Physics 2007-05-23 Jean-Pierre Amiet , Stefan Weigert

We consider the problem of sequential estimation of the unknowns of state-space and deep state-space models that include estimation of functions and latent processes of the models. The proposed approach relies on Gaussian and deep Gaussian…

Machine Learning · Computer Science 2024-03-26 Yuhao Liu , Marzieh Ajirak , Petar Djuric

This article proposes numerically robust algorithms for Gaussian state estimation with singular observation noise. Our approach combines a series of basis changes with Bayes' rule, transforming the singular estimation problem into a…

Methodology · Statistics 2025-03-14 Nicholas Krämer , Filip Tronarp

Graphical models are commonly used to represent conditional dependence relationships between variables. There are multiple methods available for exploring them from high-dimensional data, but almost all of them rely on the assumption that…

Machine Learning · Statistics 2020-04-22 Tianxi Li , Cheng Qian , Elizaveta Levina , Ji Zhu

We present a quantum algorithm for efficiently sampling transformed Gaussian random fields on $d$-dimensional domains, based on an enhanced version of the classical moving average method. Pointwise transformations enforcing boundedness are…

Quantum Physics · Physics 2025-08-20 Matthias Deiml , Daniel Peterseim

A linear Gaussian state-space smoothing algorithm is presented for estimation of derivatives from a sequence of noisy measurements. The algorithm uses numerically stable square-root formulas, can handle simultaneous independent measurements…

Methodology · Statistics 2016-10-17 Robert Piche

Generating large-scale samples of stationary random fields is of great importance in the fields such as geomaterial modeling and uncertainty quantification. Traditional methodologies based on covariance matrix decomposition have the…

Methodology · Statistics 2022-08-23 Bin Zhu , Jiahao Liu , Zhengshou Lai , Tao Qian

Efficiently certifying non-Gaussian entanglement in continuous-variable quantum systems is a central challenge for advancing quantum information processing, photonic quantum computing, and metrology. Here, we put forward continuous-variable…

Quantum Physics · Physics 2025-04-23 Serge Deside , Tobias Haas , Nicolas J. Cerf

We introduce a complete description of a multi-mode bosonic quantum state in the coherent-state basis (which in this work is denoted as "$K$" function ), which---up to a phase---is the square root of the well-known Husimi "$Q$"…

Quantum Physics · Physics 2019-05-16 Christos Gagatsos , Saikat Guha

Genuine multimode entanglement in continuous variable systems can be quantified by exploring the geometry of the state-space, namely via the generalized geometric measure (GGM) which is defined as the shortest distance of a given multimode…

Quantum Physics · Physics 2020-07-22 Saptarshi Roy , Tamoghna Das , Aditi Sen De

In this paper we propose a method to estimate the density matrix \rho of a d-level quantum system by measurements on the N-fold system. The scheme is based on covariant observables and representation theory of unitary groups and it extends…

Quantum Physics · Physics 2009-11-10 M. Keyl