Asymptotic Spectral Measures: Between Quantum Theory and E
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Singular Value Decomposition (SVD)
San José State University Math 253: Mathematical Methods for Data Visualization Lecture 5: Singular Value Decomposition (SVD) Dr. Guangliang Chen Outline • Matrix SVD Singular Value Decomposition (SVD) Introduction We have seen that symmetric matrices are always (orthogonally) diagonalizable. That is, for any symmetric matrix A ∈ Rn×n, there exist an orthogonal matrix Q = [q1 ... qn] and a diagonal matrix Λ = diag(λ1, . , λn), both real and square, such that A = QΛQT . We have pointed out that λi’s are the eigenvalues of A and qi’s the corresponding eigenvectors (which are orthogonal to each other and have unit norm). Thus, such a factorization is called the eigendecomposition of A, also called the spectral decomposition of A. What about general rectangular matrices? Dr. Guangliang Chen | Mathematics & Statistics, San José State University3/22 Singular Value Decomposition (SVD) Existence of the SVD for general matrices Theorem: For any matrix X ∈ Rn×d, there exist two orthogonal matrices U ∈ Rn×n, V ∈ Rd×d and a nonnegative, “diagonal” matrix Σ ∈ Rn×d (of the same size as X) such that T Xn×d = Un×nΣn×dVd×d. Remark. This is called the Singular Value Decomposition (SVD) of X: • The diagonals of Σ are called the singular values of X (often sorted in decreasing order). • The columns of U are called the left singular vectors of X. • The columns of V are called the right singular vectors of X. Dr. Guangliang Chen | Mathematics & Statistics, San José State University4/22 Singular Value Decomposition (SVD) * * b * b (n>d) b b b * b = * * = b b b * (n<d) * b * * b b Dr. -
Studies in the Geometry of Quantum Measurements
Irina Dumitru Studies in the Geometry of Quantum Measurements Studies in the Geometry of Quantum Measurements Studies in Irina Dumitru Irina Dumitru is a PhD student at the department of Physics at Stockholm University. She has carried out research in the field of quantum information, focusing on the geometry of Hilbert spaces. ISBN 978-91-7911-218-9 Department of Physics Doctoral Thesis in Theoretical Physics at Stockholm University, Sweden 2020 Studies in the Geometry of Quantum Measurements Irina Dumitru Academic dissertation for the Degree of Doctor of Philosophy in Theoretical Physics at Stockholm University to be publicly defended on Thursday 10 September 2020 at 13.00 in sal C5:1007, AlbaNova universitetscentrum, Roslagstullsbacken 21, and digitally via video conference (Zoom). Public link will be made available at www.fysik.su.se in connection with the nailing of the thesis. Abstract Quantum information studies quantum systems from the perspective of information theory: how much information can be stored in them, how much the information can be compressed, how it can be transmitted. Symmetric informationally- Complete POVMs are measurements that are well-suited for reading out the information in a system; they can be used to reconstruct the state of a quantum system without ambiguity and with minimum redundancy. It is not known whether such measurements can be constructed for systems of any finite dimension. Here, dimension refers to the dimension of the Hilbert space where the state of the system belongs. This thesis introduces the notion of alignment, a relation between a symmetric informationally-complete POVM in dimension d and one in dimension d(d-2), thus contributing towards the search for these measurements. -
Notes on the Spectral Theorem
Math 108b: Notes on the Spectral Theorem From section 6.3, we know that every linear operator T on a finite dimensional inner prod- uct space V has an adjoint. (T ∗ is defined as the unique linear operator on V such that hT (x), yi = hx, T ∗(y)i for every x, y ∈ V – see Theroems 6.8 and 6.9.) When V is infinite dimensional, the adjoint T ∗ may or may not exist. One useful fact (Theorem 6.10) is that if β is an orthonormal basis for a finite dimen- ∗ ∗ sional inner product space V , then [T ]β = [T ]β. That is, the matrix representation of the operator T ∗ is equal to the complex conjugate of the matrix representation for T . For a general vector space V , and a linear operator T , we have already asked the ques- tion “when is there a basis of V consisting only of eigenvectors of T ?” – this is exactly when T is diagonalizable. Now, for an inner product space V , we know how to check whether vec- tors are orthogonal, and we know how to define the norms of vectors, so we can ask “when is there an orthonormal basis of V consisting only of eigenvectors of T ?” Clearly, if there is such a basis, T is diagonalizable – and moreover, eigenvectors with distinct eigenvalues must be orthogonal. Definitions Let V be an inner product space. Let T ∈ L(V ). (a) T is normal if T ∗T = TT ∗ (b) T is self-adjoint if T ∗ = T For the next two definitions, assume V is finite-dimensional: Then, (c) T is unitary if F = C and kT (x)k = kxk for every x ∈ V (d) T is orthogonal if F = R and kT (x)k = kxk for every x ∈ V Notes 1. -
The Notion of Observable and the Moment Problem for ∗-Algebras and Their GNS Representations
The notion of observable and the moment problem for ∗-algebras and their GNS representations Nicol`oDragoa, Valter Morettib Department of Mathematics, University of Trento, and INFN-TIFPA via Sommarive 14, I-38123 Povo (Trento), Italy. [email protected], [email protected] February, 17, 2020 Abstract We address some usually overlooked issues concerning the use of ∗-algebras in quantum theory and their physical interpretation. If A is a ∗-algebra describing a quantum system and ! : A ! C a state, we focus in particular on the interpretation of !(a) as expectation value for an algebraic observable a = a∗ 2 A, studying the problem of finding a probability n measure reproducing the moments f!(a )gn2N. This problem enjoys a close relation with the selfadjointness of the (in general only symmetric) operator π!(a) in the GNS representation of ! and thus it has important consequences for the interpretation of a as an observable. We n provide physical examples (also from QFT) where the moment problem for f!(a )gn2N does not admit a unique solution. To reduce this ambiguity, we consider the moment problem n ∗ (a) for the sequences f!b(a )gn2N, being b 2 A and !b(·) := !(b · b). Letting µ!b be a n solution of the moment problem for the sequence f!b(a )gn2N, we introduce a consistency (a) relation on the family fµ!b gb2A. We prove a 1-1 correspondence between consistent families (a) fµ!b gb2A and positive operator-valued measures (POVM) associated with the symmetric (a) operator π!(a). In particular there exists a unique consistent family of fµ!b gb2A if and only if π!(a) is maximally symmetric. -
The Spectral Theorem for Self-Adjoint and Unitary Operators Michael Taylor Contents 1. Introduction 2. Functions of a Self-Adjoi
The Spectral Theorem for Self-Adjoint and Unitary Operators Michael Taylor Contents 1. Introduction 2. Functions of a self-adjoint operator 3. Spectral theorem for bounded self-adjoint operators 4. Functions of unitary operators 5. Spectral theorem for unitary operators 6. Alternative approach 7. From Theorem 1.2 to Theorem 1.1 A. Spectral projections B. Unbounded self-adjoint operators C. Von Neumann's mean ergodic theorem 1 2 1. Introduction If H is a Hilbert space, a bounded linear operator A : H ! H (A 2 L(H)) has an adjoint A∗ : H ! H defined by (1.1) (Au; v) = (u; A∗v); u; v 2 H: We say A is self-adjoint if A = A∗. We say U 2 L(H) is unitary if U ∗ = U −1. More generally, if H is another Hilbert space, we say Φ 2 L(H; H) is unitary provided Φ is one-to-one and onto, and (Φu; Φv)H = (u; v)H , for all u; v 2 H. If dim H = n < 1, each self-adjoint A 2 L(H) has the property that H has an orthonormal basis of eigenvectors of A. The same holds for each unitary U 2 L(H). Proofs can be found in xx11{12, Chapter 2, of [T3]. Here, we aim to prove the following infinite dimensional variant of such a result, called the Spectral Theorem. Theorem 1.1. If A 2 L(H) is self-adjoint, there exists a measure space (X; F; µ), a unitary map Φ: H ! L2(X; µ), and a 2 L1(X; µ), such that (1.2) ΦAΦ−1f(x) = a(x)f(x); 8 f 2 L2(X; µ): Here, a is real valued, and kakL1 = kAk. -
Quantum Minimax Theorem
Quantum Minimax Theorem Fuyuhiko TANAKA July 18, 2018 Abstract Recently, many fundamental and important results in statistical decision the- ory have been extended to the quantum system. Quantum Hunt-Stein theorem and quantum locally asymptotic normality are typical successful examples. In the present paper, we show quantum minimax theorem, which is also an extension of a well-known result, minimax theorem in statistical decision theory, first shown by Wald and generalized by Le Cam. Our assertions hold for every closed convex set of measurements and for general parametric models of density operator. On the other hand, Bayesian analysis based on least favorable priors has been widely used in classical statistics and is expected to play a crucial role in quantum statistics. According to this trend, we also show the existence of least favorable priors, which seems to be new even in classical statistics. 1 Introduction Quantum statistical inference is the inference on a quantum system from relatively small amount of measurement data. It covers also precise analysis of statistical error [34], ex- ploration of optimal measurements to extract information [27], development of efficient arXiv:1410.3639v1 [quant-ph] 14 Oct 2014 numerical computation [10]. With the rapid development of experimental techniques, there has been much work on quantum statistical inference [31], which is now applied to quantum tomography [1, 9], validation of entanglement [17], and quantum bench- marks [18, 28]. In particular, many fundamental and important results in statistical deci- sion theory [38] have been extended to the quantum system. Theoretical framework was originally established by Holevo [20, 21, 22]. -
7 Spectral Properties of Matrices
7 Spectral Properties of Matrices 7.1 Introduction The existence of directions that are preserved by linear transformations (which are referred to as eigenvectors) has been discovered by L. Euler in his study of movements of rigid bodies. This work was continued by Lagrange, Cauchy, Fourier, and Hermite. The study of eigenvectors and eigenvalues acquired in- creasing significance through its applications in heat propagation and stability theory. Later, Hilbert initiated the study of eigenvalue in functional analysis (in the theory of integral operators). He introduced the terms of eigenvalue and eigenvector. The term eigenvalue is a German-English hybrid formed from the German word eigen which means “own” and the English word “value”. It is interesting that Cauchy referred to the same concept as characteristic value and the term characteristic polynomial of a matrix (which we introduce in Definition 7.1) was derived from this naming. We present the notions of geometric and algebraic multiplicities of eigen- values, examine properties of spectra of special matrices, discuss variational characterizations of spectra and the relationships between matrix norms and eigenvalues. We conclude this chapter with a section dedicated to singular values of matrices. 7.2 Eigenvalues and Eigenvectors Let A Cn×n be a square matrix. An eigenpair of A is a pair (λ, x) C (Cn∈ 0 ) such that Ax = λx. We refer to λ is an eigenvalue and to ∈x is× an eigenvector−{ } . The set of eigenvalues of A is the spectrum of A and will be denoted by spec(A). If (λ, x) is an eigenpair of A, the linear system Ax = λx has a non-trivial solution in x. -
Veral Versions of the Spectral Theorem for Normal Operators in Hilbert Spaces
10. The Spectral Theorem The big moment has arrived, and we are now ready to prove se- veral versions of the spectral theorem for normal operators in Hilbert spaces. Throughout this chapter, it should be helpful to compare our results with the more familiar special case when the Hilbert space is finite-dimensional. In this setting, the spectral theorem says that every normal matrix T 2 Cn×n can be diagonalized by a unitary transfor- mation. This can be rephrased as follows: There are numbers zj 2 C n (the eigenvalues) and orthogonal projections Pj 2 B(C ) such that Pm T = j=1 zjPj. The subspaces R(Pj) are orthogonal to each other. From this representation of T , it is then also clear that Pj is the pro- jection onto the eigenspace belonging to zj. In fact, we have already proved one version of the (general) spec- tral theorem: The Gelfand theory of the commutative C∗-algebra A ⊆ B(H) that is generated by a normal operator T 2 B(H) provides a functional calculus: We can define f(T ), for f 2 C(σ(T )) in such a way that the map C(σ(T )) ! A, f 7! f(T ) is an isometric ∗-isomorphism between C∗-algebras, and this is the spectral theorem in one of its ma- ny disguises! See Theorem 9.13 and the discussion that follows. As a warm-up, let us use this material to give a quick proof of the result about normal matrices T 2 Cn×n that was stated above. Consider the C∗-algebra A ⊆ Cn×n that is generated by T . -
Mathematical Work of Franciszek Hugon Szafraniec and Its Impacts
Tusi Advances in Operator Theory (2020) 5:1297–1313 Mathematical Research https://doi.org/10.1007/s43036-020-00089-z(0123456789().,-volV)(0123456789().,-volV) Group ORIGINAL PAPER Mathematical work of Franciszek Hugon Szafraniec and its impacts 1 2 3 Rau´ l E. Curto • Jean-Pierre Gazeau • Andrzej Horzela • 4 5,6 7 Mohammad Sal Moslehian • Mihai Putinar • Konrad Schmu¨ dgen • 8 9 Henk de Snoo • Jan Stochel Received: 15 May 2020 / Accepted: 19 May 2020 / Published online: 8 June 2020 Ó The Author(s) 2020 Abstract In this essay, we present an overview of some important mathematical works of Professor Franciszek Hugon Szafraniec and a survey of his achievements and influence. Keywords Szafraniec Á Mathematical work Á Biography Mathematics Subject Classification 01A60 Á 01A61 Á 46-03 Á 47-03 1 Biography Professor Franciszek Hugon Szafraniec’s mathematical career began in 1957 when he left his homeland Upper Silesia for Krako´w to enter the Jagiellonian University. At that time he was 17 years old and, surprisingly, mathematics was his last-minute choice. However random this decision may have been, it was a fortunate one: he succeeded in achieving all the academic degrees up to the scientific title of professor in 1980. It turned out his choice to join the university shaped the Krako´w mathematical community. Communicated by Qingxiang Xu. & Jan Stochel [email protected] Extended author information available on the last page of the article 1298 R. E. Curto et al. Professor Franciszek H. Szafraniec Krako´w beyond Warsaw and Lwo´w belonged to the famous Polish School of Mathematics in the prewar period. -
Chapter 6 the Singular Value Decomposition Ax=B Version of 11 April 2019
Matrix Methods for Computational Modeling and Data Analytics Virginia Tech Spring 2019 · Mark Embree [email protected] Chapter 6 The Singular Value Decomposition Ax=b version of 11 April 2019 The singular value decomposition (SVD) is among the most important and widely applicable matrix factorizations. It provides a natural way to untangle a matrix into its four fundamental subspaces, and reveals the relative importance of each direction within those subspaces. Thus the singular value decomposition is a vital tool for analyzing data, and it provides a slick way to understand (and prove) many fundamental results in matrix theory. It is the perfect tool for solving least squares problems, and provides the best way to approximate a matrix with one of lower rank. These notes construct the SVD in various forms, then describe a few of its most compelling applications. 6.1 Eigenvalues and eigenvectors of symmetric matrices To derive the singular value decomposition of a general (rectangu- lar) matrix A IR m n, we shall rely on several special properties of 2 ⇥ the square, symmetric matrix ATA. While this course assumes you are well acquainted with eigenvalues and eigenvectors, we will re- call some fundamental concepts, especially pertaining to symmetric matrices. 6.1.1 A passing nod to complex numbers Recall that even if a matrix has real number entries, it could have eigenvalues that are complex numbers; the corresponding eigenvec- tors will also have complex entries. Consider, for example, the matrix 0 1 S = − . " 10# 73 To find the eigenvalues of S, form the characteristic polynomial l 1 det(lI S)=det = l2 + 1. -
ASYMPTOTICALLY ISOSPECTRAL QUANTUM GRAPHS and TRIGONOMETRIC POLYNOMIALS. Pavel Kurasov, Rune Suhr
ISSN: 1401-5617 ASYMPTOTICALLY ISOSPECTRAL QUANTUM GRAPHS AND TRIGONOMETRIC POLYNOMIALS. Pavel Kurasov, Rune Suhr Research Reports in Mathematics Number 2, 2018 Department of Mathematics Stockholm University Electronic version of this document is available at http://www.math.su.se/reports/2018/2 Date of publication: Maj 16, 2018. 2010 Mathematics Subject Classification: Primary 34L25, 81U40; Secondary 35P25, 81V99. Keywords: Quantum graphs, almost periodic functions. Postal address: Department of Mathematics Stockholm University S-106 91 Stockholm Sweden Electronic addresses: http://www.math.su.se/ [email protected] Asymptotically isospectral quantum graphs and generalised trigonometric polynomials Pavel Kurasov and Rune Suhr Dept. of Mathematics, Stockholm Univ., 106 91 Stockholm, SWEDEN [email protected], [email protected] Abstract The theory of almost periodic functions is used to investigate spectral prop- erties of Schr¨odinger operators on metric graphs, also known as quantum graphs. In particular we prove that two Schr¨odingeroperators may have asymptotically close spectra if and only if the corresponding reference Lapla- cians are isospectral. Our result implies that a Schr¨odingeroperator is isospectral to the standard Laplacian on a may be different metric graph only if the potential is identically equal to zero. Keywords: Quantum graphs, almost periodic functions 2000 MSC: 34L15, 35R30, 81Q10 Introduction. The current paper is devoted to the spectral theory of quantum graphs, more precisely to the direct and inverse spectral theory of Schr¨odingerop- erators on metric graphs [3, 20, 24]. Such operators are defined by three parameters: a finite compact metric graph Γ; • a real integrable potential q L (Γ); • ∈ 1 vertex conditions, which can be parametrised by unitary matrices S. -
Categorical Characterizations of Operator-Valued Measures
Categorical characterizations of operator-valued measures Frank Roumen Inst. for Mathematics, Astrophysics and Particle Physics (IMAPP) Radboud University Nijmegen [email protected] The most general type of measurement in quantum physics is modeled by a positive operator-valued measure (POVM). Mathematically, a POVM is a generalization of a measure, whose values are not real numbers, but positive operators on a Hilbert space. POVMs can equivalently be viewed as maps between effect algebras or as maps between algebras for the Giry monad. We will show that this equivalence is an instance of a duality between two categories. In the special case of continuous POVMs, we obtain two equivalent representations in terms of morphisms between von Neumann algebras. 1 Introduction The logic governing quantum measurements differs from classical logic, and it is still unknown which mathematical structure is the best description of quantum logic. The first attempt for such a logic was discussed in the famous paper [2], in which Birkhoff and von Neumann propose to use the orthomod- ular lattice of projections on a Hilbert space. However, this approach has been criticized for its lack of generality, see for instance [22] for an overview of experiments that do not fit in the Birkhoff-von Neumann scheme. The operational approach to quantum physics generalizes the approach based on pro- jective measurements. In this approach, all measurements should be formulated in terms of the outcome statistics of experiments. Thus the logical and probabilistic aspects of quantum mechanics are combined into a unified description. The basic concept of operational quantum mechanics is an effect on a Hilbert space, which is a positive operator lying below the identity.