Extending the Stationary Quantum Mechanics of Being to a Nonstationary Quantum Theory of Becoming and Decaying
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Quantum Errors and Disturbances: Response to Busch, Lahti and Werner
entropy Article Quantum Errors and Disturbances: Response to Busch, Lahti and Werner David Marcus Appleby Centre for Engineered Quantum Systems, School of Physics, The University of Sydney, Sydney, NSW 2006, Australia; [email protected]; Tel.: +44-734-210-5857 Academic Editors: Gregg Jaeger and Andrei Khrennikov Received: 27 February 2016; Accepted: 28 April 2016; Published: 6 May 2016 Abstract: Busch, Lahti and Werner (BLW) have recently criticized the operator approach to the description of quantum errors and disturbances. Their criticisms are justified to the extent that the physical meaning of the operator definitions has not hitherto been adequately explained. We rectify that omission. We then examine BLW’s criticisms in the light of our analysis. We argue that, although the BLW approach favour (based on the Wasserstein two-deviation) has its uses, there are important physical situations where an operator approach is preferable. We also discuss the reason why the error-disturbance relation is still giving rise to controversies almost a century after Heisenberg first stated his microscope argument. We argue that the source of the difficulties is the problem of interpretation, which is not so wholly disconnected from experimental practicalities as is sometimes supposed. Keywords: error disturbance principle; uncertainty principle; quantum measurement; Heisenberg PACS: 03.65.Ta 1. Introduction The error-disturbance principle remains highly controversial almost a century after Heisenberg wrote the paper [1], which originally suggested it. It is remarkable that this should be so, since the disagreements concern what is arguably the most fundamental concept of all, not only in physics, but in empirical science generally: namely, the concept of measurement accuracy. -
Lecture 6 — Generalized Eigenspaces & Generalized Weight
18.745 Introduction to Lie Algebras September 28, 2010 Lecture 6 | Generalized Eigenspaces & Generalized Weight Spaces Prof. Victor Kac Scribe: Andrew Geng and Wenzhe Wei Definition 6.1. Let A be a linear operator on a vector space V over field F and let λ 2 F, then the subspace N Vλ = fv j (A − λI) v = 0 for some positive integer Ng is called a generalized eigenspace of A with eigenvalue λ. Note that the eigenspace of A with eigenvalue λ is a subspace of Vλ. Example 6.1. A is a nilpotent operator if and only if V = V0. Proposition 6.1. Let A be a linear operator on a finite dimensional vector space V over an alge- braically closed field F, and let λ1; :::; λs be all eigenvalues of A, n1; n2; :::; ns be their multiplicities. Then one has the generalized eigenspace decomposition: s M V = Vλi where dim Vλi = ni i=1 Proof. By the Jordan normal form of A in some basis e1; e2; :::en. Its matrix is of the following form: 0 1 Jλ1 B Jλ C A = B 2 C B .. C @ . A ; Jλn where Jλi is an ni × ni matrix with λi on the diagonal, 0 or 1 in each entry just above the diagonal, and 0 everywhere else. Let Vλ1 = spanfe1; e2; :::; en1 g;Vλ2 = spanfen1+1; :::; en1+n2 g; :::; so that Jλi acts on Vλi . i.e. Vλi are A-invariant and Aj = λ I + N , N nilpotent. Vλi i ni i i From the above discussion, we obtain the following decomposition of the operator A, called the classical Jordan decomposition A = As + An where As is the operator which in the basis above is the diagonal part of A, and An is the rest (An = A − As). -
Calculus and Differential Equations II
Calculus and Differential Equations II MATH 250 B Linear systems of differential equations Linear systems of differential equations Calculus and Differential Equations II Second order autonomous linear systems We are mostly interested with2 × 2 first order autonomous systems of the form x0 = a x + b y y 0 = c x + d y where x and y are functions of t and a, b, c, and d are real constants. Such a system may be re-written in matrix form as d x x a b = M ; M = : dt y y c d The purpose of this section is to classify the dynamics of the solutions of the above system, in terms of the properties of the matrix M. Linear systems of differential equations Calculus and Differential Equations II Existence and uniqueness (general statement) Consider a linear system of the form dY = M(t)Y + F (t); dt where Y and F (t) are n × 1 column vectors, and M(t) is an n × n matrix whose entries may depend on t. Existence and uniqueness theorem: If the entries of the matrix M(t) and of the vector F (t) are continuous on some open interval I containing t0, then the initial value problem dY = M(t)Y + F (t); Y (t ) = Y dt 0 0 has a unique solution on I . In particular, this means that trajectories in the phase space do not cross. Linear systems of differential equations Calculus and Differential Equations II General solution The general solution to Y 0 = M(t)Y + F (t) reads Y (t) = C1 Y1(t) + C2 Y2(t) + ··· + Cn Yn(t) + Yp(t); = U(t) C + Yp(t); where 0 Yp(t) is a particular solution to Y = M(t)Y + F (t). -
A Stepwise Planned Approach to the Solution of Hilbert's Sixth Problem. II: Supmech and Quantum Systems
A Stepwise Planned Approach to the Solution of Hilbert’s Sixth Problem. II : Supmech and Quantum Systems Tulsi Dass Indian Statistical Institute, Delhi Centre, 7, SJS Sansanwal Marg, New Delhi, 110016, India. E-mail: [email protected]; [email protected] Abstract: Supmech, which is noncommutative Hamiltonian mechanics (NHM) (developed in paper I) with two extra ingredients : positive ob- servable valued measures (PObVMs) [which serve to connect state-induced expectation values and classical probabilities] and the ‘CC condition’ [which stipulates that the sets of observables and pure states be mutually separating] is proposed as a universal mechanics potentially covering all physical phe- nomena. It facilitates development of an autonomous formalism for quantum mechanics. Quantum systems, defined algebraically as supmech Hamiltonian systems with non-supercommutative system algebras, are shown to inevitably have Hilbert space based realizations (so as to accommodate rigged Hilbert space based Dirac bra-ket formalism), generally admitting commutative su- perselection rules. Traditional features of quantum mechanics of finite parti- cle systems appear naturally. A treatment of localizability much simpler and more general than the traditional one is given. Treating massive particles as localizable elementary quantum systems, the Schr¨odinger wave functions with traditional Born interpretation appear as natural objects for the descrip- tion of their pure states and the Schr¨odinger equation for them is obtained without ever using a classical Hamiltonian or Lagrangian. A provisional set of axioms for the supmech program is given. arXiv:1002.2061v4 [math-ph] 18 Dec 2010 1 I. Introduction This is the second of a series of papers aimed at obtaining a solution of Hilbert’s sixth problem in the framework of a noncommutative geome- try (NCG) based ‘all-embracing’ scheme of mechanics. -
SUPPLEMENT on EIGENVALUES and EIGENVECTORS We Give
SUPPLEMENT ON EIGENVALUES AND EIGENVECTORS We give some extra material on repeated eigenvalues and complex eigenvalues. 1. REPEATED EIGENVALUES AND GENERALIZED EIGENVECTORS For repeated eigenvalues, it is not always the case that there are enough eigenvectors. Let A be an n × n real matrix, with characteristic polynomial m1 mk pA(λ) = (λ1 − λ) ··· (λk − λ) with λ j 6= λ` for j 6= `. Use the following notation for the eigenspace, E(λ j ) = {v : (A − λ j I)v = 0 }. We also define the generalized eigenspace for the eigenvalue λ j by gen m j E (λ j ) = {w : (A − λ j I) w = 0 }, where m j is the multiplicity of the eigenvalue. A vector in E(λ j ) is called a generalized eigenvector. The following is a extension of theorem 7 in the book. 0 m1 mk Theorem (7 ). Let A be an n × n matrix with characteristic polynomial pA(λ) = (λ1 − λ) ··· (λk − λ) , where λ j 6= λ` for j 6= `. Then, the following hold. gen (a) dim(E(λ j )) ≤ m j and dim(E (λ j )) = m j for 1 ≤ j ≤ k. If λ j is complex, then these dimensions are as subspaces of Cn. gen n (b) If B j is a basis for E (λ j ) for 1 ≤ j ≤ k, then B1 ∪ · · · ∪ Bk is a basis of C , i.e., there is always a basis of generalized eigenvectors for all the eigenvalues. If the eigenvalues are all real all the vectors are real, then this gives a basis of Rn. (c) Assume A is a real matrix and all its eigenvalues are real. -
Math 2280 - Lecture 23
Math 2280 - Lecture 23 Dylan Zwick Fall 2013 In our last lecture we dealt with solutions to the system: ′ x = Ax where A is an n × n matrix with n distinct eigenvalues. As promised, today we will deal with the question of what happens if we have less than n distinct eigenvalues, which is what happens if any of the roots of the characteristic polynomial are repeated. This lecture corresponds with section 5.4 of the textbook, and the as- signed problems from that section are: Section 5.4 - 1, 8, 15, 25, 33 The Case of an Order 2 Root Let’s start with the case an an order 2 root.1 So, our eigenvalue equation has a repeated root, λ, of multiplicity 2. There are two ways this can go. The first possibility is that we have two distinct (linearly independent) eigenvectors associated with the eigen- value λ. In this case, all is good, and we just use these two eigenvectors to create two distinct solutions. 1Admittedly, not one of Sherlock Holmes’s more popular mysteries. 1 Example - Find a general solution to the system: 9 4 0 ′ x = −6 −1 0 x 6 4 3 Solution - The characteristic equation of the matrix A is: |A − λI| = (5 − λ)(3 − λ)2. So, A has the distinct eigenvalue λ1 = 5 and the repeated eigenvalue λ2 =3 of multiplicity 2. For the eigenvalue λ1 =5 the eigenvector equation is: 4 4 0 a 0 (A − 5I)v = −6 −6 0 b = 0 6 4 −2 c 0 which has as an eigenvector 1 v1 = −1 . -
Riesz-Like Bases in Rigged Hilbert Spaces, in Preparation [14] Bonet, J., Fern´Andez, C., Galbis, A
RIESZ-LIKE BASES IN RIGGED HILBERT SPACES GIORGIA BELLOMONTE AND CAMILLO TRAPANI Abstract. The notions of Bessel sequence, Riesz-Fischer sequence and Riesz basis are generalized to a rigged Hilbert space D[t] ⊂H⊂D×[t×]. A Riesz- like basis, in particular, is obtained by considering a sequence {ξn}⊂D which is mapped by a one-to-one continuous operator T : D[t] → H[k · k] into an orthonormal basis of the central Hilbert space H of the triplet. The operator T is, in general, an unbounded operator in H. If T has a bounded inverse then the rigged Hilbert space is shown to be equivalent to a triplet of Hilbert spaces. 1. Introduction Riesz bases (i.e., sequences of elements ξn of a Hilbert space which are trans- formed into orthonormal bases by some bounded{ } operator withH bounded inverse) often appear as eigenvectors of nonself-adjoint operators. The simplest situation is the following one. Let H be a self-adjoint operator with discrete spectrum defined on a subset D(H) of the Hilbert space . Assume, to be more definite, that each H eigenvalue λn is simple. Then the corresponding eigenvectors en constitute an orthonormal basis of . If X is another operator similar to H,{ i.e.,} there exists a bounded operator T withH bounded inverse T −1 which intertwines X and H, in the sense that T : D(H) D(X) and XT ξ = T Hξ, for every ξ D(H), then, as it is → ∈ easily seen, the vectors ϕn with ϕn = Ten are eigenvectors of X and constitute a Riesz basis for . -
Spectral Theory
SPECTRAL THEORY EVAN JENKINS Abstract. These are notes from two lectures given in MATH 27200, Basic Functional Analysis, at the University of Chicago in March 2010. The proof of the spectral theorem for compact operators comes from [Zim90, Chapter 3]. 1. The Spectral Theorem for Compact Operators The idea of the proof of the spectral theorem for compact self-adjoint operators on a Hilbert space is very similar to the finite-dimensional case. Namely, we first show that an eigenvector exists, and then we show that there is an orthonormal basis of eigenvectors by an inductive argument (in the form of an application of Zorn's lemma). We first recall a few facts about self-adjoint operators. Proposition 1.1. Let V be a Hilbert space, and T : V ! V a bounded, self-adjoint operator. (1) If W ⊂ V is a T -invariant subspace, then W ? is T -invariant. (2) For all v 2 V , hT v; vi 2 R. (3) Let Vλ = fv 2 V j T v = λvg. Then Vλ ? Vµ whenever λ 6= µ. We will need one further technical fact that characterizes the norm of a self-adjoint operator. Lemma 1.2. Let V be a Hilbert space, and T : V ! V a bounded, self-adjoint operator. Then kT k = supfjhT v; vij j kvk = 1g. Proof. Let α = supfjhT v; vij j kvk = 1g. Evidently, α ≤ kT k. We need to show the other direction. Given v 2 V with T v 6= 0, setting w0 = T v=kT vk gives jhT v; w0ij = kT vk. -
Quantum Mechanical Observables Under a Symplectic Transformation
Quantum Mechanical Observables under a Symplectic Transformation of Coordinates Jakub K´aninsk´y1 1Charles University, Faculty of Mathematics and Physics, Institute of Theoretical Physics. E-mail address: [email protected] March 17, 2021 We consider a general symplectic transformation (also known as linear canonical transformation) of quantum-mechanical observables in a quan- tized version of a finite-dimensional system with configuration space iso- morphic to Rq. Using the formalism of rigged Hilbert spaces, we define eigenstates for all the observables. Then we work out the explicit form of the corresponding transformation of these eigenstates. A few examples are included at the end of the paper. 1 Introduction From a mathematical perspective we can view quantum mechanics as a science of finite- dimensional quantum systems, i.e. systems whose classical pre-image has configuration space of finite dimension. The single most important representative from this family is a quantized version of the classical system whose configuration space is isomorphic to Rq with some q N, which corresponds e.g. to the system of finitely many coupled ∈ harmonic oscillators. Classically, the state of such system is given by a point in the arXiv:2007.10858v2 [quant-ph] 16 Mar 2021 phase space, which is a vector space of dimension 2q equipped with the symplectic form. One would typically prefer to work in the abstract setting, without the need to choose any particular set of coordinates: in principle this is possible. In practice though, one will often end up choosing a symplectic basis in the phase space aligned with the given symplectic form and resort to the coordinate description. -
Operators in Rigged Hilbert Spaces: Some Spectral Properties
OPERATORS IN RIGGED HILBERT SPACES: SOME SPECTRAL PROPERTIES GIORGIA BELLOMONTE, SALVATORE DI BELLA, AND CAMILLO TRAPANI Abstract. A notion of resolvent set for an operator acting in a rigged Hilbert space D⊂H⊂D× is proposed. This set depends on a family of intermediate locally convex spaces living between D and D×, called interspaces. Some properties of the resolvent set and of the correspond- ing multivalued resolvent function are derived and some examples are discussed. 1. Introduction Spaces of linear maps acting on a rigged Hilbert space (RHS, for short) × D⊂H⊂D have often been considered in the literature both from a pure mathematical point of view [22, 23, 28, 31] and for their applications to quantum theo- ries (generalized eigenvalues, resonances of Schr¨odinger operators, quantum fields...) [8, 13, 12, 15, 14, 16, 25]. The spaces of test functions and the dis- tributions over them constitute relevant examples of rigged Hilbert spaces and operators acting on them are a fundamental tool in several problems in Analysis (differential operators with singular coefficients, Fourier trans- arXiv:1309.4038v1 [math.FA] 16 Sep 2013 forms) and also provide the basic background for the study of the problem of the multiplication of distributions by the duality method [24, 27, 34]. Before going forth, we fix some notations and basic definitions. Let be a dense linear subspace of Hilbert space and t a locally convex D H topology on , finer than the topology induced by the Hilbert norm. Then D the space of all continuous conjugate linear functionals on [t], i.e., D× D the conjugate dual of [t], is a linear vector space and contains , in the D H sense that can be identified with a subspace of . -
Generalized Eigenfunction Expansions
Universitat¨ Karlsruhe (TH) Fakult¨atf¨urMathematik Institut f¨urAnalysis Arbeitsgruppe Funktionalanalysis Generalized Eigenfunction Expansions Diploma Thesis of Emin Karayel supervised by Prof. Dr. Lutz Weis August 2009 Erkl¨arung Hiermit erkl¨areich, dass ich diese Diplomarbeit selbst¨andigverfasst habe und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe. Emin Karayel, August 2009 Anschrift Emin Karayel Werner-Siemens-Str. 21 75173 Pforzheim Matr.-Nr.: 1061189 E-Mail: [email protected] 2 Contents 1 Introduction 5 2 Eigenfunction expansions on Hilbert spaces 9 2.1 Spectral Measures . 9 2.2 Phillips Theorem . 11 2.3 Rigged Hilbert Spaces . 12 2.4 Basic Eigenfunction Expansion . 13 2.5 Extending the Operator . 15 2.6 Orthogonalizing Generalized Eigenvectors . 16 2.7 Summary . 21 3 Hilbert-Schmidt-Riggings 23 3.1 Unconditional Hilbert-Schmidt Riggings . 23 3.1.1 Hilbert-Schmidt-Integral Operators . 24 3.2 Weighted Spaces and Sobolev Spaces . 25 3.3 Conditional Hilbert-Schmidt Riggings . 26 3.4 Eigenfunction Expansion for the Laplacian . 27 3.4.1 Optimality . 27 3.5 Nuclear Riggings . 28 4 Operator Relations 31 4.1 Vector valued multiplication operators . 31 4.2 Preliminaries . 33 4.3 Interpolation spaces . 37 4.4 Eigenfunction expansion . 38 4.5 Semigroups . 41 4.6 Expansion for Homogeneous Elliptic Selfadjoint Differential Oper- ators . 42 4.7 Conclusions . 44 5 Appendix 47 5.1 Acknowledgements . 47 5.2 Notation . 47 5.3 Conventions . 49 3 1 Introduction Eigenvectors and eigenvalues are indispensable tools to investigate (at least com- pact) normal linear operators. They provide a diagonalization of the operator i.e. -
23. Eigenvalues and Eigenvectors
23. Eigenvalues and Eigenvectors 11/17/20 Eigenvalues and eigenvectors have a variety of uses. They allow us to solve linear difference and differential equations. For many non-linear equations, they inform us about the long-run behavior of the system. They are also useful for defining functions of matrices. 23.1 Eigenvalues We start with eigenvalues. Eigenvalues and Spectrum. Let A be an m m matrix. An eigenvalue (characteristic value, proper value) of A is a number λ so that× A λI is singular. The spectrum of A, σ(A)= {eigenvalues of A}.− Sometimes it’s possible to find eigenvalues by inspection of the matrix. ◮ Example 23.1.1: Some Eigenvalues. Suppose 1 0 2 1 A = , B = . 0 2 1 2 Here it is pretty obvious that subtracting either I or 2I from A yields a singular matrix. As for matrix B, notice that subtracting I leaves us with two identical columns (and rows), so 1 is an eigenvalue. Less obvious is the fact that subtracting 3I leaves us with linearly independent columns (and rows), so 3 is also an eigenvalue. We’ll see in a moment that 2 2 matrices have at most two eigenvalues, so we have determined the spectrum of each:× σ(A)= {1, 2} and σ(B)= {1, 3}. ◭ 2 MATH METHODS 23.2 Finding Eigenvalues: 2 2 × We have several ways to determine whether a matrix is singular. One method is to check the determinant. It is zero if and only the matrix is singular. That means we can find the eigenvalues by solving the equation det(A λI)=0.