Joint Measurability Through Naimark's Theorem
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
Inverse and Implicit Function Theorems for Noncommutative
Inverse and Implicit Function Theorems for Noncommutative Functions on Operator Domains Mark E. Mancuso Abstract Classically, a noncommutative function is defined on a graded domain of tuples of square matrices. In this note, we introduce a notion of a noncommutative function defined on a domain Ω ⊂ B(H)d, where H is an infinite dimensional Hilbert space. Inverse and implicit function theorems in this setting are established. When these operatorial noncommutative functions are suitably continuous in the strong operator topology, a noncommutative dilation-theoretic construction is used to show that the assumptions on their derivatives may be relaxed from boundedness below to injectivity. Keywords: Noncommutive functions, operator noncommutative functions, free anal- ysis, inverse and implicit function theorems, strong operator topology, dilation theory. MSC (2010): Primary 46L52; Secondary 47A56, 47J07. INTRODUCTION Polynomials in d noncommuting indeterminates can naturally be evaluated on d-tuples of square matrices of any size. The resulting function is graded (tuples of n × n ma- trices are mapped to n × n matrices) and preserves direct sums and similarities. Along with polynomials, noncommutative rational functions and power series, the convergence of which has been studied for example in [9], [14], [15], serve as prototypical examples of a more general class of functions called noncommutative functions. The theory of non- commutative functions finds its origin in the 1973 work of J. L. Taylor [17], who studied arXiv:1804.01040v2 [math.FA] 7 Aug 2019 the functional calculus of noncommuting operators. Roughly speaking, noncommutative functions are to polynomials in noncommuting variables as holomorphic functions from complex analysis are to polynomials in commuting variables. -
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. -
Asymptotic Spectral Measures: Between Quantum Theory and E
Asymptotic Spectral Measures: Between Quantum Theory and E-theory Jody Trout Department of Mathematics Dartmouth College Hanover, NH 03755 Email: [email protected] Abstract— We review the relationship between positive of classical observables to the C∗-algebra of quantum operator-valued measures (POVMs) in quantum measurement observables. See the papers [12]–[14] and theB books [15], C∗ E theory and asymptotic morphisms in the -algebra -theory of [16] for more on the connections between operator algebra Connes and Higson. The theory of asymptotic spectral measures, as introduced by Martinez and Trout [1], is integrally related K-theory, E-theory, and quantization. to positive asymptotic morphisms on locally compact spaces In [1], Martinez and Trout showed that there is a fundamen- via an asymptotic Riesz Representation Theorem. Examples tal quantum-E-theory relationship by introducing the concept and applications to quantum physics, including quantum noise of an asymptotic spectral measure (ASM or asymptotic PVM) models, semiclassical limits, pure spin one-half systems and quantum information processing will also be discussed. A~ ~ :Σ ( ) { } ∈(0,1] →B H I. INTRODUCTION associated to a measurable space (X, Σ). (See Definition 4.1.) In the von Neumann Hilbert space model [2] of quantum Roughly, this is a continuous family of POV-measures which mechanics, quantum observables are modeled as self-adjoint are “asymptotically” projective (or quasiprojective) as ~ 0: → operators on the Hilbert space of states of the quantum system. ′ ′ A~(∆ ∆ ) A~(∆)A~(∆ ) 0 as ~ 0 The Spectral Theorem relates this theoretical view of a quan- ∩ − → → tum observable to the more operational one of a projection- for certain measurable sets ∆, ∆′ Σ. -
Algebra Homomorphisms and the Functional Calculus
Pacific Journal of Mathematics ALGEBRA HOMOMORPHISMS AND THE FUNCTIONAL CALCULUS MARK PHILLIP THOMAS Vol. 79, No. 1 May 1978 PACIFIC JOURNAL OF MATHEMATICS Vol. 79, No. 1, 1978 ALGEBRA HOMOMORPHISMS AND THE FUNCTIONAL CALCULUS MARC THOMAS Let b be a fixed element of a commutative Banach algebra with unit. Suppose σ(b) has at most countably many connected components. We give necessary and sufficient conditions for b to possess a discontinuous functional calculus. Throughout, let B be a commutative Banach algebra with unit 1 and let rad (B) denote the radical of B. Let b be a fixed element of B. Let έ? denote the LF space of germs of functions analytic in a neighborhood of σ(6). By a functional calculus for b we mean an algebra homomorphism θr from έ? to B such that θ\z) = b and θ\l) = 1. We do not require θr to be continuous. It is well-known that if θ' is continuous, then it is equal to θ, the usual functional calculus obtained by integration around contours i.e., θ{f) = -±τ \ f(t)(f - ]dt for f eέ?, Γ a contour about σ(b) [1, 1.4.8, Theorem 3]. In this paper we investigate the conditions under which a functional calculus & is necessarily continuous, i.e., when θ is the unique functional calculus. In the first section we work with sufficient conditions. If S is any closed subspace of B such that bS Q S, we let D(b, S) denote the largest algebraic subspace of S satisfying (6 — X)D(b, S) = D(b, S)f all λeC. -
The Weyl Functional Calculus F(4)
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector JOURNAL OF FUNCTIONAL ANALYSIS 4, 240-267 (1969) The Weyl Functional Calculus ROBERT F. V. ANDERSON Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 Communicated by Edward Nelson Received July, 1968 I. INTRODUCTION In this paper a functional calculus for an n-tuple of noncommuting self-adjoint operators on a Banach space will be proposed and examined. The von Neumann spectral theorem for self-adjoint operators on a Hilbert space leads to a functional calculus which assigns to a self- adjoint operator A and a real Borel-measurable function f of a real variable, another self-adjoint operatorf(A). This calculus generalizes in a natural way to an n-tuple of com- muting self-adjoint operators A = (A, ,..., A,), because their spectral families commute. In particular, if v is an eigenvector of A, ,..., A, with eigenvalues A, ,..., A, , respectively, and f a continuous function of n real variables, f(A 1 ,..., A,) u = f(b , . U 0. This principle fails when the operators don’t commute; instead we have the Uncertainty Principle, and so on. However, the Fourier inversion formula can also be used to define the von Neumann functional cakuius for a single operator: f(4) = (37F2 1 (Sf)(&) exp( -2 f A,) 4 E’ where This definition generalizes naturally to an n-tuple of operators, commutative or not, as follows: in the n-dimensional Fourier inversion 240 THE WEYL FUNCTIONAL CALCULUS 241 formula, the free variables x 1 ,..,, x, are replaced by self-adjoint operators A, ,..., A, . -
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. -
The Holomorphic Functional Calculus Approach to Operator Semigroups
THE HOLOMORPHIC FUNCTIONAL CALCULUS APPROACH TO OPERATOR SEMIGROUPS CHARLES BATTY, MARKUS HAASE, AND JUNAID MUBEEN In honour of B´elaSz.-Nagy (29 July 1913 { 21 December 1998) on the occasion of his centenary Abstract. In this article we construct a holomorphic functional calculus for operators of half-plane type and show how key facts of semigroup theory (Hille- Yosida and Gomilko-Shi-Feng generation theorems, Trotter-Kato approxima- tion theorem, Euler approximation formula, Gearhart-Pr¨usstheorem) can be elegantly obtained in this framework. Then we discuss the notions of bounded H1-calculus and m-bounded calculus on half-planes and their relation to weak bounded variation conditions over vertical lines for powers of the resolvent. Finally we discuss the Hilbert space case, where semigroup generation is char- acterised by the operator having a strong m-bounded calculus on a half-plane. 1. Introduction The theory of strongly continuous semigroups is more than 60 years old, with the fundamental result of Hille and Yosida dating back to 1948. Several monographs and textbooks cover material which is now canonical, each of them having its own particular point of view. One may focus entirely on the semigroup, and consider the generator as a derived concept (as in [12]) or one may start with the generator and view the semigroup as the inverse Laplace transform of the resolvent (as in [2]). Right from the beginning, functional calculus methods played an important role in semigroup theory. Namely, given a C0-semigroup T = (T (t))t≥0 which is uni- formly bounded, say, one can form the averages Z 1 Tµ := T (s) µ(ds) (strong integral) 0 for µ 2 M(R+) a complex Radon measure of bounded variation on R+ = [0; 1).