Matrices That Commute with a Permutation Matrix
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Lecture Notes: Qubit Representations and Rotations
Phys 711 Topics in Particles & Fields | Spring 2013 | Lecture 1 | v0.3 Lecture notes: Qubit representations and rotations Jeffrey Yepez Department of Physics and Astronomy University of Hawai`i at Manoa Watanabe Hall, 2505 Correa Road Honolulu, Hawai`i 96822 E-mail: [email protected] www.phys.hawaii.edu/∼yepez (Dated: January 9, 2013) Contents mathematical object (an abstraction of a two-state quan- tum object) with a \one" state and a \zero" state: I. What is a qubit? 1 1 0 II. Time-dependent qubits states 2 jqi = αj0i + βj1i = α + β ; (1) 0 1 III. Qubit representations 2 A. Hilbert space representation 2 where α and β are complex numbers. These complex B. SU(2) and O(3) representations 2 numbers are called amplitudes. The basis states are or- IV. Rotation by similarity transformation 3 thonormal V. Rotation transformation in exponential form 5 h0j0i = h1j1i = 1 (2a) VI. Composition of qubit rotations 7 h0j1i = h1j0i = 0: (2b) A. Special case of equal angles 7 In general, the qubit jqi in (1) is said to be in a superpo- VII. Example composite rotation 7 sition state of the two logical basis states j0i and j1i. If References 9 α and β are complex, it would seem that a qubit should have four free real-valued parameters (two magnitudes and two phases): I. WHAT IS A QUBIT? iθ0 α φ0 e jqi = = iθ1 : (3) Let us begin by introducing some notation: β φ1 e 1 state (called \minus" on the Bloch sphere) Yet, for a qubit to contain only one classical bit of infor- 0 mation, the qubit need only be unimodular (normalized j1i = the alternate symbol is |−i 1 to unity) α∗α + β∗β = 1: (4) 0 state (called \plus" on the Bloch sphere) 1 Hence it lives on the complex unit circle, depicted on the j0i = the alternate symbol is j+i: 0 top of Figure 1. -
Diagonalizing a Matrix
Diagonalizing a Matrix Definition 1. We say that two square matrices A and B are similar provided there exists an invertible matrix P so that . 2. We say a matrix A is diagonalizable if it is similar to a diagonal matrix. Example 1. The matrices and are similar matrices since . We conclude that is diagonalizable. 2. The matrices and are similar matrices since . After we have developed some additional theory, we will be able to conclude that the matrices and are not diagonalizable. Theorem Suppose A, B and C are square matrices. (1) A is similar to A. (2) If A is similar to B, then B is similar to A. (3) If A is similar to B and if B is similar to C, then A is similar to C. Proof of (3) Since A is similar to B, there exists an invertible matrix P so that . Also, since B is similar to C, there exists an invertible matrix R so that . Now, and so A is similar to C. Thus, “A is similar to B” is an equivalence relation. Theorem If A is similar to B, then A and B have the same eigenvalues. Proof Since A is similar to B, there exists an invertible matrix P so that . Now, Since A and B have the same characteristic equation, they have the same eigenvalues. > Example Find the eigenvalues for . Solution Since is similar to the diagonal matrix , they have the same eigenvalues. Because the eigenvalues of an upper (or lower) triangular matrix are the entries on the main diagonal, we see that the eigenvalues for , and, hence, are . -
Math 511 Advanced Linear Algebra Spring 2006
MATH 511 ADVANCED LINEAR ALGEBRA SPRING 2006 Sherod Eubanks HOMEWORK 2 x2:1 : 2; 5; 9; 12 x2:3 : 3; 6 x2:4 : 2; 4; 5; 9; 11 Section 2:1: Unitary Matrices Problem 2 If ¸ 2 σ(U) and U 2 Mn is unitary, show that j¸j = 1. Solution. If ¸ 2 σ(U), U 2 Mn is unitary, and Ux = ¸x for x 6= 0, then by Theorem 2:1:4(g), we have kxkCn = kUxkCn = k¸xkCn = j¸jkxkCn , hence j¸j = 1, as desired. Problem 5 Show that the permutation matrices in Mn are orthogonal and that the permutation matrices form a sub- group of the group of real orthogonal matrices. How many different permutation matrices are there in Mn? Solution. By definition, a matrix P 2 Mn is called a permutation matrix if exactly one entry in each row n and column is equal to 1, and all other entries are 0. That is, letting ei 2 C denote the standard basis n th element of C that has a 1 in the i row and zeros elsewhere, and Sn be the set of all permutations on n th elements, then P = [eσ(1) j ¢ ¢ ¢ j eσ(n)] = Pσ for some permutation σ 2 Sn such that σ(k) denotes the k member of σ. Observe that for any σ 2 Sn, and as ½ 1 if i = j eT e = σ(i) σ(j) 0 otherwise for 1 · i · j · n by the definition of ei, we have that 2 3 T T eσ(1)eσ(1) ¢ ¢ ¢ eσ(1)eσ(n) T 6 . -
Parametrization of 3×3 Unitary Matrices Based on Polarization
Parametrization of 33 unitary matrices based on polarization algebra (May, 2018) José J. Gil Parametrization of 33 unitary matrices based on polarization algebra José J. Gil Universidad de Zaragoza. Pedro Cerbuna 12, 50009 Zaragoza Spain [email protected] Abstract A parametrization of 33 unitary matrices is presented. This mathematical approach is inspired by polarization algebra and is formulated through the identification of a set of three orthonormal three-dimensional Jones vectors representing respective pure polarization states. This approach leads to the representation of a 33 unitary matrix as an orthogonal similarity transformation of a particular type of unitary matrix that depends on six independent parameters, while the remaining three parameters correspond to the orthogonal matrix of the said transformation. The results obtained are applied to determine the structure of the second component of the characteristic decomposition of a 33 positive semidefinite Hermitian matrix. 1 Introduction In many branches of Mathematics, Physics and Engineering, 33 unitary matrices appear as key elements for solving a great variety of problems, and therefore, appropriate parameterizations in terms of minimum sets of nine independent parameters are required for the corresponding mathematical treatment. In this way, some interesting parametrizations have been obtained [1-8]. In particular, the Cabibbo-Kobayashi-Maskawa matrix (CKM matrix) [6,7], which represents information on the strength of flavour-changing weak decays and depends on four parameters, constitutes the core of a family of parametrizations of a 33 unitary matrix [8]. In this paper, a new general parametrization is presented, which is inspired by polarization algebra [9] through the structure of orthonormal sets of three-dimensional Jones vectors [10]. -
Chapter Four Determinants
Chapter Four Determinants In the first chapter of this book we considered linear systems and we picked out the special case of systems with the same number of equations as unknowns, those of the form T~x = ~b where T is a square matrix. We noted a distinction between two classes of T ’s. While such systems may have a unique solution or no solutions or infinitely many solutions, if a particular T is associated with a unique solution in any system, such as the homogeneous system ~b = ~0, then T is associated with a unique solution for every ~b. We call such a matrix of coefficients ‘nonsingular’. The other kind of T , where every linear system for which it is the matrix of coefficients has either no solution or infinitely many solutions, we call ‘singular’. Through the second and third chapters the value of this distinction has been a theme. For instance, we now know that nonsingularity of an n£n matrix T is equivalent to each of these: ² a system T~x = ~b has a solution, and that solution is unique; ² Gauss-Jordan reduction of T yields an identity matrix; ² the rows of T form a linearly independent set; ² the columns of T form a basis for Rn; ² any map that T represents is an isomorphism; ² an inverse matrix T ¡1 exists. So when we look at a particular square matrix, the question of whether it is nonsingular is one of the first things that we ask. This chapter develops a formula to determine this. (Since we will restrict the discussion to square matrices, in this chapter we will usually simply say ‘matrix’ in place of ‘square matrix’.) More precisely, we will develop infinitely many formulas, one for 1£1 ma- trices, one for 2£2 matrices, etc. -
3.3 Diagonalization
3.3 Diagonalization −4 1 1 1 Let A = 0 1. Then 0 1 and 0 1 are eigenvectors of A, with corresponding @ 4 −4 A @ 2 A @ −2 A eigenvalues −2 and −6 respectively (check). This means −4 1 1 1 −4 1 1 1 0 1 0 1 = −2 0 1 ; 0 1 0 1 = −6 0 1 : @ 4 −4 A @ 2 A @ 2 A @ 4 −4 A @ −2 A @ −2 A Thus −4 1 1 1 1 1 −2 −6 0 1 0 1 = 0−2 0 1 − 6 0 11 = 0 1 @ 4 −4 A @ 2 −2 A @ @ −2 A @ −2 AA @ −4 12 A We have −4 1 1 1 1 1 −2 0 0 1 0 1 = 0 1 0 1 @ 4 −4 A @ 2 −2 A @ 2 −2 A @ 0 −6 A 1 1 (Think about this). Thus AE = ED where E = 0 1 has the eigenvectors of A as @ 2 −2 A −2 0 columns and D = 0 1 is the diagonal matrix having the eigenvalues of A on the @ 0 −6 A main diagonal, in the order in which their corresponding eigenvectors appear as columns of E. Definition 3.3.1 A n × n matrix is A diagonal if all of its non-zero entries are located on its main diagonal, i.e. if Aij = 0 whenever i =6 j. Diagonal matrices are particularly easy to handle computationally. If A and B are diagonal n × n matrices then the product AB is obtained from A and B by simply multiplying entries in corresponding positions along the diagonal, and AB = BA. -
R'kj.Oti-1). (3) the Object of the Present Article Is to Make This Estimate Effective
TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 259, Number 2, June 1980 EFFECTIVE p-ADIC BOUNDS FOR SOLUTIONS OF HOMOGENEOUS LINEAR DIFFERENTIAL EQUATIONS BY B. DWORK AND P. ROBBA Dedicated to K. Iwasawa Abstract. We consider a finite set of power series in one variable with coefficients in a field of characteristic zero having a chosen nonarchimedean valuation. We study the growth of these series near the boundary of their common "open" disk of convergence. Our results are definitive when the wronskian is bounded. The main application involves local solutions of ordinary linear differential equations with analytic coefficients. The effective determination of the common radius of conver- gence remains open (and is not treated here). Let K be an algebraically closed field of characteristic zero complete under a nonarchimedean valuation with residue class field of characteristic p. Let D = d/dx L = D"+Cn_lD'-l+ ■ ■■ +C0 (1) be a linear differential operator with coefficients meromorphic in some neighbor- hood of the origin. Let u = a0 + a,jc + . (2) be a power series solution of L which converges in an open (/>-adic) disk of radius r. Our object is to describe the asymptotic behavior of \a,\rs as s —*oo. In a series of articles we have shown that subject to certain restrictions we may conclude that r'KJ.Oti-1). (3) The object of the present article is to make this estimate effective. At the same time we greatly simplify, and generalize, our best previous results [12] for the noneffective form. Our previous work was based on the notion of a generic disk together with a condition for reducibility of differential operators with unbounded solutions [4, Theorem 4]. -
Diagonalizable Matrix - Wikipedia, the Free Encyclopedia
Diagonalizable matrix - Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Matrix_diagonalization Diagonalizable matrix From Wikipedia, the free encyclopedia (Redirected from Matrix diagonalization) In linear algebra, a square matrix A is called diagonalizable if it is similar to a diagonal matrix, i.e., if there exists an invertible matrix P such that P −1AP is a diagonal matrix. If V is a finite-dimensional vector space, then a linear map T : V → V is called diagonalizable if there exists a basis of V with respect to which T is represented by a diagonal matrix. Diagonalization is the process of finding a corresponding diagonal matrix for a diagonalizable matrix or linear map.[1] A square matrix which is not diagonalizable is called defective. Diagonalizable matrices and maps are of interest because diagonal matrices are especially easy to handle: their eigenvalues and eigenvectors are known and one can raise a diagonal matrix to a power by simply raising the diagonal entries to that same power. Geometrically, a diagonalizable matrix is an inhomogeneous dilation (or anisotropic scaling) — it scales the space, as does a homogeneous dilation, but by a different factor in each direction, determined by the scale factors on each axis (diagonal entries). Contents 1 Characterisation 2 Diagonalization 3 Simultaneous diagonalization 4 Examples 4.1 Diagonalizable matrices 4.2 Matrices that are not diagonalizable 4.3 How to diagonalize a matrix 4.3.1 Alternative Method 5 An application 5.1 Particular application 6 Quantum mechanical application 7 See also 8 Notes 9 References 10 External links Characterisation The fundamental fact about diagonalizable maps and matrices is expressed by the following: An n-by-n matrix A over the field F is diagonalizable if and only if the sum of the dimensions of its eigenspaces is equal to n, which is the case if and only if there exists a basis of Fn consisting of eigenvectors of A. -
EIGENVALUES and EIGENVECTORS 1. Diagonalizable Linear Transformations and Matrices Recall, a Matrix, D, Is Diagonal If It Is
EIGENVALUES AND EIGENVECTORS 1. Diagonalizable linear transformations and matrices Recall, a matrix, D, is diagonal if it is square and the only non-zero entries are on the diagonal. This is equivalent to D~ei = λi~ei where here ~ei are the standard n n vector and the λi are the diagonal entries. A linear transformation, T : R ! R , is n diagonalizable if there is a basis B of R so that [T ]B is diagonal. This means [T ] is n×n similar to the diagonal matrix [T ]B. Similarly, a matrix A 2 R is diagonalizable if it is similar to some diagonal matrix D. To diagonalize a linear transformation is to find a basis B so that [T ]B is diagonal. To diagonalize a square matrix is to find an invertible S so that S−1AS = D is diagonal. Fix a matrix A 2 Rn×n We say a vector ~v 2 Rn is an eigenvector if (1) ~v 6= 0. (2) A~v = λ~v for some scalar λ 2 R. The scalar λ is the eigenvalue associated to ~v or just an eigenvalue of A. Geo- metrically, A~v is parallel to ~v and the eigenvalue, λ. counts the stretching factor. Another way to think about this is that the line L := span(~v) is left invariant by multiplication by A. n An eigenbasis of A is a basis, B = (~v1; : : : ;~vn) of R so that each ~vi is an eigenvector of A. Theorem 1.1. The matrix A is diagonalizable if and only if there is an eigenbasis of A. -
Rotation Matrix - Wikipedia, the Free Encyclopedia Page 1 of 22
Rotation matrix - Wikipedia, the free encyclopedia Page 1 of 22 Rotation matrix From Wikipedia, the free encyclopedia In linear algebra, a rotation matrix is a matrix that is used to perform a rotation in Euclidean space. For example the matrix rotates points in the xy -Cartesian plane counterclockwise through an angle θ about the origin of the Cartesian coordinate system. To perform the rotation, the position of each point must be represented by a column vector v, containing the coordinates of the point. A rotated vector is obtained by using the matrix multiplication Rv (see below for details). In two and three dimensions, rotation matrices are among the simplest algebraic descriptions of rotations, and are used extensively for computations in geometry, physics, and computer graphics. Though most applications involve rotations in two or three dimensions, rotation matrices can be defined for n-dimensional space. Rotation matrices are always square, with real entries. Algebraically, a rotation matrix in n-dimensions is a n × n special orthogonal matrix, i.e. an orthogonal matrix whose determinant is 1: . The set of all rotation matrices forms a group, known as the rotation group or the special orthogonal group. It is a subset of the orthogonal group, which includes reflections and consists of all orthogonal matrices with determinant 1 or -1, and of the special linear group, which includes all volume-preserving transformations and consists of matrices with determinant 1. Contents 1 Rotations in two dimensions 1.1 Non-standard orientation -
§9.2 Orthogonal Matrices and Similarity Transformations
n×n Thm: Suppose matrix Q 2 R is orthogonal. Then −1 T I Q is invertible with Q = Q . n T T I For any x; y 2 R ,(Q x) (Q y) = x y. n I For any x 2 R , kQ xk2 = kxk2. Ex 0 1 1 1 1 1 2 2 2 2 B C B 1 1 1 1 C B − 2 2 − 2 2 C B C T H = B C ; H H = I : B C B − 1 − 1 1 1 C B 2 2 2 2 C @ A 1 1 1 1 2 − 2 − 2 2 x9.2 Orthogonal Matrices and Similarity Transformations n×n Def: A matrix Q 2 R is said to be orthogonal if its columns n (1) (2) (n)o n q ; q ; ··· ; q form an orthonormal set in R . Ex 0 1 1 1 1 1 2 2 2 2 B C B 1 1 1 1 C B − 2 2 − 2 2 C B C T H = B C ; H H = I : B C B − 1 − 1 1 1 C B 2 2 2 2 C @ A 1 1 1 1 2 − 2 − 2 2 x9.2 Orthogonal Matrices and Similarity Transformations n×n Def: A matrix Q 2 R is said to be orthogonal if its columns n (1) (2) (n)o n q ; q ; ··· ; q form an orthonormal set in R . n×n Thm: Suppose matrix Q 2 R is orthogonal. Then −1 T I Q is invertible with Q = Q . n T T I For any x; y 2 R ,(Q x) (Q y) = x y. -
Alternating Sign Matrices, Extensions and Related Cones
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/311671190 Alternating sign matrices, extensions and related cones Article in Advances in Applied Mathematics · May 2017 DOI: 10.1016/j.aam.2016.12.001 CITATIONS READS 0 29 2 authors: Richard A. Brualdi Geir Dahl University of Wisconsin–Madison University of Oslo 252 PUBLICATIONS 3,815 CITATIONS 102 PUBLICATIONS 1,032 CITATIONS SEE PROFILE SEE PROFILE Some of the authors of this publication are also working on these related projects: Combinatorial matrix theory; alternating sign matrices View project All content following this page was uploaded by Geir Dahl on 16 December 2016. The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately. Alternating sign matrices, extensions and related cones Richard A. Brualdi∗ Geir Dahly December 1, 2016 Abstract An alternating sign matrix, or ASM, is a (0; ±1)-matrix where the nonzero entries in each row and column alternate in sign, and where each row and column sum is 1. We study the convex cone generated by ASMs of order n, called the ASM cone, as well as several related cones and polytopes. Some decomposition results are shown, and we find a minimal Hilbert basis of the ASM cone. The notion of (±1)-doubly stochastic matrices and a generalization of ASMs are introduced and various properties are shown. For instance, we give a new short proof of the linear characterization of the ASM polytope, in fact for a more general polytope.