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Penalized estimation principle is fundamental to high-dimensional problems. In the literature, it has been extensively and successfully applied to various models with only structural parameters. As a contrast, in this paper, we apply this…

Statistics Theory · Mathematics 2017-08-03 Jianqing Fan , Runlong Tang , Xiaofeng Shi

The von Neumann entanglement entropy is studied with the density-matrix renormalization group technique. We propose a simple approach to calculate the central charge using the entanglement entropy for one-dimensional (1D) quantum system.…

Strongly Correlated Electrons · Physics 2015-05-30 Satoshi Nishimoto

The paper introduces a penalized matrix estimation procedure aiming at solutions which are sparse and low-rank at the same time. Such structures arise in the context of social networks or protein interactions where underlying graphs have…

Data Structures and Algorithms · Computer Science 2012-07-03 Emile Richard , Pierre-Andre Savalle , Nicolas Vayatis

A state $\rho=(\rho_n)_{n=1}^{\infty}$ is a sequence such that $\rho_n$ is a density matrix on $n$ qubits. It formalizes the notion of an infinite sequence of qubits. The von Neumann entropy $H(d)$ of a density matrix $d$ is the Shannon…

Quantum Physics · Physics 2025-04-15 Tejas Bhojraj

The entropy of a quantum system is a measure of its randomness, and has applications in measuring quantum entanglement. We study the problem of measuring the von Neumann entropy, $S(\rho)$, and R\'enyi entropy, $S_\alpha(\rho)$ of an…

Quantum Physics · Physics 2022-04-19 Jayadev Acharya , Ibrahim Issa , Nirmal V. Shende , Aaron B. Wagner

Estimator selection has become a crucial issue in non parametric estimation. Two widely used methods are penalized empirical risk minimization (such as penalized log-likelihood estimation) or pairwise comparison (such as Lepski's method).…

Statistics Theory · Mathematics 2017-10-19 Claire Lacour , Pascal Massart , Vincent Rivoirard

Reporting about the density matrix theory for a composite quantum system of flavor eigenstates, we introduce the idea of flavor-weighted energies. It provides us with the right correlation between the energies of flavor eigenstates and…

High Energy Physics - Phenomenology · Physics 2013-06-19 Alex E. Bernardini

We give a numerical characterization of mutual orthogonality (that is, complementarity) for subalgebras. In order to give such a characterization for mutually orthogonal subalgebras $A$ and $B$ of the $k \times k$ matrix algebra…

Operator Algebras · Mathematics 2014-09-15 Marie Choda

In this paper we investigate the von Neumann entropy in the ground state of one-dimensional anyonic systems with the repulsive interaction. Based on the Bethe-ansatz method, the entanglement properties for the arbitrary statistical…

Quantum Physics · Physics 2014-11-20 Hongli Guo , Yajiang Hao , Shu Chen

Matrix completion algorithms recover a low rank matrix from a small fraction of the entries, each entry contaminated with additive errors. In practice, the singular vectors and singular values of the low rank matrix play a pivotal role for…

Methodology · Statistics 2016-05-03 Juhee Cho , Donggyu Kim , Karl Rohe

In this paper, we propose an original approach to stochastic control problems. We consider a weak formulation that is written as an optimization (minimization) problem on the space of probability measures. We then introduce a penalized…

Optimization and Control · Mathematics 2025-08-05 Thibaut Bourdais , Nadia Oudjane , Francesco Russo

Based oh the properties of Lie algebras, in this work we develop a general framework to linearize the von-Neumann equation rendering it in a suitable form for quantum simulations. We show that one of these linearizations of the von-Neumann…

Quantum Physics · Physics 2024-02-08 Alejandro Kunold

Measuring incomplete sets of mutually unbiased bases constitutes a sensible approach to the tomography of high-dimensional quantum systems. The unbiased nature of these bases optimizes the uncertainty hypervolume. However, imposing…

Quantum Physics · Physics 2015-11-11 J. Rehacek , Z. Hradil , Y. S. Teo , L. L. Sanchez-Soto , H. K. Ng , J. H. Chai , B. -G. Englert

We study one-dimensional systems of $N$ particles in a one-dimensional harmonic trap with an inverse power law interaction $\sim|x|^{-d}$. Within the framework of the harmonic approximation we derive, in the strong interaction limit, the…

Quantum Physics · Physics 2017-07-18 Przemyslaw Koscik

The entropic way of formulating Heisenberg's uncertainty principle not only plays a fundamental role in applications of quantum information theory but also is essential for manifesting genuine nonclassical features of quantum systems. In…

Quantum Physics · Physics 2024-03-05 Shan Huang , Hua-Lei Yin , Zeng-Bing Chen , Shengjun Wu

Uncertainty relations provide constraints on how well the outcomes of incompatible measurements can be predicted, and, as well as being fundamental to our understanding of quantum theory, they have practical applications such as for…

Quantum Physics · Physics 2013-05-30 Patrick J. Coles , Roger Colbeck , Li Yu , Michael Zwolak

We develop notions of Rota-Baxter structures and associated Birkhoff factorizations, in the context of min-plus semirings and their thermodynamic deformations, including deformations arising from quantum information measures such as the von…

Quantum Algebra · Mathematics 2015-12-09 Matilde Marcolli , Nicolas Tedeschi

We present a simple comparative framework for testing and developing uncertainty modeling in uncertain marching cubes implementations. The selection of a model to represent the probability distribution of uncertain values directly…

Human-Computer Interaction · Computer Science 2024-09-16 Robert Sisneros , Tushar M. Athawale , David Pugmire , Kenneth Moreland

A bottleneck for analyzing the interplay between magic and entanglement is the computation of these quantities in highly entangled quantum many-body magic states. Efficient extraction of entanglement can also inform our understanding of…

Quantum Physics · Physics 2025-10-09 ChunJun Cao , Gong Cheng , Tianci Zhou

Noisy matrix completion aims at estimating a low-rank matrix given only partial and corrupted entries. Despite substantial progress in designing efficient estimation algorithms, it remains largely unclear how to assess the uncertainty of…

Machine Learning · Statistics 2019-11-15 Yuxin Chen , Jianqing Fan , Cong Ma , Yuling Yan
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