The Modern Tools of Quantum Mechanics
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Banach J. Math. Anal. 2 (2008), No. 2, 59–67 . OPERATOR-VALUED
Banach J. Math. Anal. 2 (2008), no. 2, 59–67 Banach Journal of Mathematical Analysis ISSN: 1735-8787 (electronic) http://www.math-analysis.org . OPERATOR-VALUED INNER PRODUCT AND OPERATOR INEQUALITIES JUN ICHI FUJII1 This paper is dedicated to Professor Josip E. Peˇcari´c Submitted by M. S. Moslehian Abstract. The Schwarz inequality and Jensen’s one are fundamental in a Hilbert space. Regarding a sesquilinear map B(X, Y ) = Y ∗X as an operator- valued inner product, we discuss operator versions for the above inequalities and give simple conditions that the equalities hold. 1. Introduction Inequality plays a basic role in analysis and consequently in Mathematics. As surveyed briefly in [6], operator inequalities on a Hilbert space have been discussed particularly since Furuta inequality was established. But it is not easy in general to give a simple condition that the equality in an operator inequality holds. In this note, we observe basic operator inequalities and discuss the equality conditions. To show this, we consider simple linear algebraic structure in operator spaces: For Hilbert spaces H and K, the symbol B(H, K) denotes all the (bounded linear) operators from H to K and B(H) ≡ B(H, H). Then, consider an operator n n matrix A = (Aij) ∈ B(H ), a vector X = (Xj) ∈ B(H, H ) with operator entries Xj ∈ B(H), an operator-valued inner product n ∗ X ∗ Y X = Yj Xj, j=1 Date: Received: 29 March 2008; Accepted 13 May 2008. 2000 Mathematics Subject Classification. Primary 47A63; Secondary 47A75, 47A80. Key words and phrases. Schwarz inequality, Jensen inequality, Operator inequality. -
A Mini-Introduction to Information Theory
A Mini-Introduction To Information Theory Edward Witten School of Natural Sciences, Institute for Advanced Study Einstein Drive, Princeton, NJ 08540 USA Abstract This article consists of a very short introduction to classical and quantum information theory. Basic properties of the classical Shannon entropy and the quantum von Neumann entropy are described, along with related concepts such as classical and quantum relative entropy, conditional entropy, and mutual information. A few more detailed topics are considered in the quantum case. arXiv:1805.11965v5 [hep-th] 4 Oct 2019 Contents 1 Introduction 2 2 Classical Information Theory 2 2.1 ShannonEntropy ................................... .... 2 2.2 ConditionalEntropy ................................. .... 4 2.3 RelativeEntropy .................................... ... 6 2.4 Monotonicity of Relative Entropy . ...... 7 3 Quantum Information Theory: Basic Ingredients 10 3.1 DensityMatrices .................................... ... 10 3.2 QuantumEntropy................................... .... 14 3.3 Concavity ......................................... .. 16 3.4 Conditional and Relative Quantum Entropy . ....... 17 3.5 Monotonicity of Relative Entropy . ...... 20 3.6 GeneralizedMeasurements . ...... 22 3.7 QuantumChannels ................................... ... 24 3.8 Thermodynamics And Quantum Channels . ...... 26 4 More On Quantum Information Theory 27 4.1 Quantum Teleportation and Conditional Entropy . ......... 28 4.2 Quantum Relative Entropy And Hypothesis Testing . ......... 32 4.3 Encoding -
Quantum Information Theory
Lecture Notes Quantum Information Theory Renato Renner with contributions by Matthias Christandl February 6, 2013 Contents 1 Introduction 5 2 Probability Theory 6 2.1 What is probability? . .6 2.2 Definition of probability spaces and random variables . .7 2.2.1 Probability space . .7 2.2.2 Random variables . .7 2.2.3 Notation for events . .8 2.2.4 Conditioning on events . .8 2.3 Probability theory with discrete random variables . .9 2.3.1 Discrete random variables . .9 2.3.2 Marginals and conditional distributions . .9 2.3.3 Special distributions . 10 2.3.4 Independence and Markov chains . 10 2.3.5 Functions of random variables, expectation values, and Jensen's in- equality . 10 2.3.6 Trace distance . 11 2.3.7 I.i.d. distributions and the law of large numbers . 12 2.3.8 Channels . 13 3 Information Theory 15 3.1 Quantifying information . 15 3.1.1 Approaches to define information and entropy . 15 3.1.2 Entropy of events . 16 3.1.3 Entropy of random variables . 17 3.1.4 Conditional entropy . 18 3.1.5 Mutual information . 19 3.1.6 Smooth min- and max- entropies . 20 3.1.7 Shannon entropy as a special case of min- and max-entropy . 20 3.2 An example application: channel coding . 21 3.2.1 Definition of the problem . 21 3.2.2 The general channel coding theorem . 21 3.2.3 Channel coding for i.i.d. channels . 24 3.2.4 The converse . 24 2 4 Quantum States and Operations 26 4.1 Preliminaries . -
Computing the Polar Decomposition---With Applications
This 1984 technical report was published as N. J. Higham. Computing the polar decomposition---with applications. SIAM J. Sci. Stat. Comput., 7(4):1160-1174, 1986. The appendix contains an alternative proof omitted in the published paper. Pages 19-20 are missing from the copy that was scanned. The report is mainly of interest for how it was produced. - The cover was prepared in Vizawrite 64 on a Commodore 64 and was printed on an Epson FX-80 dot matrix printer. - The main text was prepared in Vuwriter on an Apricot PC and printed on an unknown dot matrix printer. - The bibliography was prepared in Superbase on a Commodore 64 and printed on an Epson FX-80 dot matrix printer. COMPUTING THE POLAR DECOMPOSITION - WITH APPLICATIONS N.J. Higham • Numerical Analysis Report No. 94 November 1984 This work was carried out with the support of a SERC Research Studentship • Department of Mathematics University of Manchester Manchester M13 9PL ENGLAND Univeriity of Manchester/UMIST Joint Nu•erical Analysis Reports D•p•rt••nt of Math••atic1 Dep•rt•ent of Mathe~atic1 The Victoria Univ•rsity University of Manchester Institute of of Manchester Sci•nce •nd Technology Reque1t1 for individu•l t•chnical reports •ay be addressed to Dr C.T.H. Baker, Depart•ent of Mathe••tics, The University, Mancheiter M13 9PL ABSTRACT .. A quadratically convergent Newton method for computing the polar decomposition of a full-rank matrix is presented and analysed. Acceleration parameters are introduced so as to enhance the initial rate of convergence and it is shown how reliable estimates of the optimal parameters may be computed in practice. -
Geometry of Matrix Decompositions Seen Through Optimal Transport and Information Geometry
Published in: Journal of Geometric Mechanics doi:10.3934/jgm.2017014 Volume 9, Number 3, September 2017 pp. 335{390 GEOMETRY OF MATRIX DECOMPOSITIONS SEEN THROUGH OPTIMAL TRANSPORT AND INFORMATION GEOMETRY Klas Modin∗ Department of Mathematical Sciences Chalmers University of Technology and University of Gothenburg SE-412 96 Gothenburg, Sweden Abstract. The space of probability densities is an infinite-dimensional Rie- mannian manifold, with Riemannian metrics in two flavors: Wasserstein and Fisher{Rao. The former is pivotal in optimal mass transport (OMT), whereas the latter occurs in information geometry|the differential geometric approach to statistics. The Riemannian structures restrict to the submanifold of multi- variate Gaussian distributions, where they induce Riemannian metrics on the space of covariance matrices. Here we give a systematic description of classical matrix decompositions (or factorizations) in terms of Riemannian geometry and compatible principal bundle structures. Both Wasserstein and Fisher{Rao geometries are discussed. The link to matrices is obtained by considering OMT and information ge- ometry in the category of linear transformations and multivariate Gaussian distributions. This way, OMT is directly related to the polar decomposition of matrices, whereas information geometry is directly related to the QR, Cholesky, spectral, and singular value decompositions. We also give a coherent descrip- tion of gradient flow equations for the various decompositions; most flows are illustrated in numerical examples. The paper is a combination of previously known and original results. As a survey it covers the Riemannian geometry of OMT and polar decomposi- tions (smooth and linear category), entropy gradient flows, and the Fisher{Rao metric and its geodesics on the statistical manifold of multivariate Gaussian distributions. -
Quantum Channels and Operations
1 Quantum information theory (20110401) Lecturer: Jens Eisert Chapter 4: Quantum channels 2 Contents 4 Quantum channels 5 4.1 General quantum state transformations . .5 4.2 Complete positivity . .5 4.2.1 Choi-Jamiolkowski isomorphism . .7 4.2.2 Kraus’ theorem and Stinespring dilations . .9 4.2.3 Disturbance versus information gain . 10 4.3 Local operations and classical communication . 11 4.3.1 One-local operations . 11 4.3.2 General local quantum operations . 12 3 4 CONTENTS Chapter 4 Quantum channels 4.1 General quantum state transformations What is the most general transformation of a quantum state that one can perform? One might wonder what this question is supposed to mean: We already know of uni- tary Schrodinger¨ evolution generated by some Hamiltonian H. We also know of the measurement postulate that alters a state upon measurement. So what is the question supposed to mean? In fact, this change in mindset we have already encountered above, when we thought of unitary operations. Of course, one can interpret this a-posteriori as being generated by some Hamiltonian, but this is not so much the point. The em- phasis here is on what can be done, what unitary state transformations are possible. It is the purpose of this chapter to bring this mindset to a completion, and to ask what kind of state transformations are generally possible in quantum mechanics. There is an abstract, mathematically minded approach to the question, introducing notions of complete positivity. Contrasting this, one can think of putting ingredients of unitary evolution and measurement together. -
Quantum Mechanics Quick Reference
Quantum Mechanics Quick Reference Ivan Damg˚ard This note presents a few basic concepts and notation from quantum mechanics in very condensed form. It is not the intention that you should read all of it immediately. The text is meant as a source from where you can quickly recontruct the meaning of some of the basic concepts, without having to trace through too much material. 1 The Postulates Quantum mechanics is a mathematical model of the physical world, which is built on a small number of postulates, each of which makes a particular claim on how the model reflects physical reality. All the rest of the theory can be derived from these postulates, but the postulates themselves cannot be proved in the mathematical sense. One has to make experiments and see whether the predictions of the theory are confirmed. To understand what the postulates say about computation, it is useful to think of how we describe classical computation. One way to do this is to say that a computer can be in one of a number of possible states, represented by all internal registers etc. We then do a number of operations, each of which takes the computer from one state to another, where the sequence of operations is determined by the input. And finally we get a result by observing which state we have arrived in. In this context, the postulates say that a classical computer is a special case of some- thing more general, namely a quantum computer. More concretely they say how one describes the states a quantum computer can be in, which operations we can do, and finally how we can read out the result. -
Computing the Polar Decomposition—With Applications
Computing the Polar Decomposition—with Applications Higham, Nicholas J. 1986 MIMS EPrint: 2007.9 Manchester Institute for Mathematical Sciences School of Mathematics The University of Manchester Reports available from: http://eprints.maths.manchester.ac.uk/ And by contacting: The MIMS Secretary School of Mathematics The University of Manchester Manchester, M13 9PL, UK ISSN 1749-9097 SIAM J. Sci. STAT. COMPUT. (C) 1986 Society for Industrial and Applied Mathematics Vol. 7, No. 4, October 1986 OO7 COMPUTING THE POLAR DECOMPOSITION---WITH APPLICATIONS* NICHOLAS J. HIGHAMf Abstract. A quadratically convergent Newton method for computing the polar decomposition of a full-rank matrix is presented and analysed. Acceleration parameters are introduced so as to enhance the initial rate of convergence and it is shown how reliable estimates of the optimal parameters may be computed in practice. To add to the known best approximation property of the unitary polar factor, the Hermitian polar factor H of a nonsingular Hermitian matrix A is shown to be a good positive definite approximation to A and 1/2(A / H) is shown to be a best Hermitian positive semi-definite approximation to A. Perturbation bounds for the polar factors are derived. Applications of the polar decomposition to factor analysis, aerospace computations and optimisation are outlined; and a new method is derived for computing the square root of a symmetric positive definite matrix. Key words, polar decomposition, singular value decomposition, Newton's method, matrix square root AMS(MOS) subject classifications. 65F25, 65F30, 65F35 1. Introduction. The polar decomposition is a generalisation to matrices of the familiar complex number representation z r e i, r ->_ 0. -
NEW FORMULAS for the SPECTRAL RADIUS VIA ALUTHGE TRANSFORM Fadil Chabbabi, Mostafa Mbekhta
NEW FORMULAS FOR THE SPECTRAL RADIUS VIA ALUTHGE TRANSFORM Fadil Chabbabi, Mostafa Mbekhta To cite this version: Fadil Chabbabi, Mostafa Mbekhta. NEW FORMULAS FOR THE SPECTRAL RADIUS VIA ALUTHGE TRANSFORM. 2016. hal-01334044 HAL Id: hal-01334044 https://hal.archives-ouvertes.fr/hal-01334044 Preprint submitted on 20 Jun 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. NEW FORMULAS FOR THE SPECTRAL RADIUS VIA ALUTHGE TRANSFORM FADIL CHABBABI AND MOSTAFA MBEKHTA Abstract. In this paper we give several expressions of spectral radius of a bounded operator on a Hilbert space, in terms of iterates of Aluthge transformation, numerical radius and the asymptotic behavior of the powers of this operator. Also we obtain several characterizations of normaloid operators. 1. Introduction Let H be complex Hilbert spaces and B(H) be the Banach space of all bounded linear operators from H into it self. For T ∈ B(H), the spectrum of T is denoted by σ(T) and r(T) its spectral radius. We denote also by W(T) and w(T) the numerical range and the numerical radius of T. As usually, for T ∈B(H) we denote the module of T by |T| = (T ∗T)1/2 and we shall always write, without further mention, T = U|T| to be the unique polar decomposition of T, where U is the appropriate partial isometry satisfying N(U) = N(T). -
Spectral and Polar Decomposition in AW*-Algebras
Spectral and Polar Decomposition in AW*-Algebras M. Frank We show the possibility and the uniqueness of polar decomposition of elements of arbitrary AW*-algebras inside them. We prove that spectral decomposition of normal elements of normal AW*-algebras is possible and unique inside them. The possibility of spectral decomposition of normal elements does not depend on the normality of the AW*-algebra under consideration. Key words: Operator algebras, monotone complete C*-algebras, AW*-algebras, spectral decom- position and polar decomposition of operators AMS subject classification: 46L05, 46L35, 47C15 The spectral decomposition of normal linear (bounded) operators and the polar decom- position of arbitrary linear (bounded) operators on Hilbert spaces have been interesting and technically useful results in operator theory [3, 9, 13, 20]. The development of the concept of von Neumann algebras on Hilbert spaces has shown that both these decompo- sitions are always possible inside of the appropriate von Neumann algebra [14]. New light on these assertions was shed identifying the class of von Neumann algebras among all C*-algebras by the class of C*-algebras which possess a pre-dual Banach space, the W*- algebras. The possibility of C*-theoretical representationless descriptions of spectral and arXiv:funct-an/9303001v1 11 Mar 1993 polar decomposition of elements of von Neumann algebras (and may be of more general C*-algebras) inside them has been opened up. Steps to get results in this direction were made by several authors. The W*-case was investigated by S. Sakai in 1958-60, [18, 19]. Later on J. D. M. Wright has considered spectral decomposition of normal elements of embeddable AW*-algebras, i.e., of AW*-algebras possessing a faithful von Neumann type representation on a self-dual Hilbert A-module over a commutative AW*-algebra A (on a so called Kaplansky–Hilbert module), [23, 24]. -
Partial Traces and Entropy Inequalities Rajendra Bhatia Indian Statistical Institute, 7, S.J.S
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Linear Algebra and its Applications 370 (2003) 125–132 www.elsevier.com/locate/laa Partial traces and entropy inequalities Rajendra Bhatia Indian Statistical Institute, 7, S.J.S. Sansanwal Marg, New Delhi 110016, India Received 8 May 2002; accepted 5 January 2003 Submitted by C. Davis Abstract The partial trace operation and the strong subadditivity property of entropy in quantum mechanics are explained in linear algebra terms. © 2003 Elsevier Inc. All rights reserved. Keywords: Quantum entropy; Density matrix; Partial trace; Operator convexity; Matrix inequalities 1. Introduction There is an interesting matrix operation called partial trace in the physics lit- erature. The first goal of this short expository paper is to connect this operation to others more familiar to linear algebraists. The second goal is to use this connection to present a slightly simpler proof of an important theorem of Lieb and Ruskai [11,12] called the strong subadditivity (SSA) of quantum-mechanical entropy. Closely re- lated to SSA, and a crucial ingredient in one of its proofs, is another theorem of Lieb [9] called Lieb’s concavity theorem (solution of the Wigner–Yanase–Dyson conjecture). An interesting alternate formulation and proof of this latter theorem appeared in a paper of Ando [1] well-known to linear algebraists. Discussion of the Lieb–Ruskai theorem, however, seems to have remained confined to the physics literature. We believe there is much of interest here for others as well, particularly for those interested in linear algebra. -
Arxiv:1211.0058V1 [Math-Ph] 31 Oct 2012 Ydfiiina Operator an Definition by Nay4h6,(70)(35Q80)
NOTE ON THE SPECTRAL THEOREM T. L. GILL AND D. WILLIAMS Abstract. In this note, we show that the spectral theorem, has two representations; the Stone-von Neumann representation and one based on the polar decomposition of linear operators, which we call the de- formed representation. The deformed representation has the advantage that it provides an easy extension to all closed densely defined linear operators on Hilbert space. Furthermore, the deformed representation can also be extended all separable reflexive Banach spaces and has a limited extension to non-reflexive Banach spaces. Introduction Let C[B] be the closed densely defined linear operators on a Banach space. By definition an operator A, defined on a separable Banach space B is of Baire class one if it can be approximated by a sequence {An} ⊂ L[B], of arXiv:1211.0058v1 [math-ph] 31 Oct 2012 bounded linear operators. If B is a Hilbert space, then every A ∈C[B] is of Baire class one. However, it turns out that, if B is not a Hilbert space, there may be operators A ∈C[B] that are not of Baire class one. 1991 Mathematics Subject Classification. Primary (46B03), (47D03) Sec- ondary(47H06), (47F05) (35Q80). Key words and phrases. spectral theorem, vector measures, vector-valued functions, Reflexive Banach spaces. 1 2 GILL AND WILLIAMS A Banach space B is said to be quasi-reflexive if dim{B′′/B} < ∞, and nonquasi-reflexive if dim{B′′/B} = ∞. Vinokurov, Petunin and Pliczko [VPP] have shown that, for every nonquasi-reflexive Banach space B, there is a closed densely defined linear operator A which is not of Baire class one (for example, C[0, 1] or L1[Rn], n ∈ N).