On the Decidability of Membership in Matrix-Exponential Semigroups
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Quantum Information
Quantum Information J. A. Jones Michaelmas Term 2010 Contents 1 Dirac Notation 3 1.1 Hilbert Space . 3 1.2 Dirac notation . 4 1.3 Operators . 5 1.4 Vectors and matrices . 6 1.5 Eigenvalues and eigenvectors . 8 1.6 Hermitian operators . 9 1.7 Commutators . 10 1.8 Unitary operators . 11 1.9 Operator exponentials . 11 1.10 Physical systems . 12 1.11 Time-dependent Hamiltonians . 13 1.12 Global phases . 13 2 Quantum bits and quantum gates 15 2.1 The Bloch sphere . 16 2.2 Density matrices . 16 2.3 Propagators and Pauli matrices . 18 2.4 Quantum logic gates . 18 2.5 Gate notation . 21 2.6 Quantum networks . 21 2.7 Initialization and measurement . 23 2.8 Experimental methods . 24 3 An atom in a laser field 25 3.1 Time-dependent systems . 25 3.2 Sudden jumps . 26 3.3 Oscillating fields . 27 3.4 Time-dependent perturbation theory . 29 3.5 Rabi flopping and Fermi's Golden Rule . 30 3.6 Raman transitions . 32 3.7 Rabi flopping as a quantum gate . 32 3.8 Ramsey fringes . 33 3.9 Measurement and initialisation . 34 1 CONTENTS 2 4 Spins in magnetic fields 35 4.1 The nuclear spin Hamiltonian . 35 4.2 The rotating frame . 36 4.3 On-resonance excitation . 38 4.4 Excitation phases . 38 4.5 Off-resonance excitation . 39 4.6 Practicalities . 40 4.7 The vector model . 40 4.8 Spin echoes . 41 4.9 Measurement and initialisation . 42 5 Photon techniques 43 5.1 Spatial encoding . -
MATH 237 Differential Equations and Computer Methods
Queen’s University Mathematics and Engineering and Mathematics and Statistics MATH 237 Differential Equations and Computer Methods Supplemental Course Notes Serdar Y¨uksel November 19, 2010 This document is a collection of supplemental lecture notes used for Math 237: Differential Equations and Computer Methods. Serdar Y¨uksel Contents 1 Introduction to Differential Equations 7 1.1 Introduction: ................................... ....... 7 1.2 Classification of Differential Equations . ............... 7 1.2.1 OrdinaryDifferentialEquations. .......... 8 1.2.2 PartialDifferentialEquations . .......... 8 1.2.3 Homogeneous Differential Equations . .......... 8 1.2.4 N-thorderDifferentialEquations . ......... 8 1.2.5 LinearDifferentialEquations . ......... 8 1.3 Solutions of Differential equations . .............. 9 1.4 DirectionFields................................. ........ 10 1.5 Fundamental Questions on First-Order Differential Equations............... 10 2 First-Order Ordinary Differential Equations 11 2.1 ExactDifferentialEquations. ........... 11 2.2 MethodofIntegratingFactors. ........... 12 2.3 SeparableDifferentialEquations . ............ 13 2.4 Differential Equations with Homogenous Coefficients . ................ 13 2.5 First-Order Linear Differential Equations . .............. 14 2.6 Applications.................................... ....... 14 3 Higher-Order Ordinary Linear Differential Equations 15 3.1 Higher-OrderDifferentialEquations . ............ 15 3.1.1 LinearIndependence . ...... 16 3.1.2 WronskianofasetofSolutions . ........ 16 3.1.3 Non-HomogeneousProblem -
The Exponential of a Matrix
5-28-2012 The Exponential of a Matrix The solution to the exponential growth equation dx kt = kx is given by x = c e . dt 0 It is natural to ask whether you can solve a constant coefficient linear system ′ ~x = A~x in a similar way. If a solution to the system is to have the same form as the growth equation solution, it should look like At ~x = e ~x0. The first thing I need to do is to make sense of the matrix exponential eAt. The Taylor series for ez is ∞ n z z e = . n! n=0 X It converges absolutely for all z. It A is an n × n matrix with real entries, define ∞ n n At t A e = . n! n=0 X The powers An make sense, since A is a square matrix. It is possible to show that this series converges for all t and every matrix A. Differentiating the series term-by-term, ∞ ∞ ∞ ∞ n−1 n n−1 n n−1 n−1 m m d At t A t A t A t A At e = n = = A = A = Ae . dt n! (n − 1)! (n − 1)! m! n=0 n=1 n=1 m=0 X X X X At ′ This shows that e solves the differential equation ~x = A~x. The initial condition vector ~x(0) = ~x0 yields the particular solution At ~x = e ~x0. This works, because e0·A = I (by setting t = 0 in the power series). Another familiar property of ordinary exponentials holds for the matrix exponential: If A and B com- mute (that is, AB = BA), then A B A B e e = e + . -
Matrix Lie Groups
Maths Seminar 2007 MATRIX LIE GROUPS Claudiu C Remsing Dept of Mathematics (Pure and Applied) Rhodes University Grahamstown 6140 26 September 2007 RhodesUniv CCR 0 Maths Seminar 2007 TALK OUTLINE 1. What is a matrix Lie group ? 2. Matrices revisited. 3. Examples of matrix Lie groups. 4. Matrix Lie algebras. 5. A glimpse at elementary Lie theory. 6. Life beyond elementary Lie theory. RhodesUniv CCR 1 Maths Seminar 2007 1. What is a matrix Lie group ? Matrix Lie groups are groups of invertible • matrices that have desirable geometric features. So matrix Lie groups are simultaneously algebraic and geometric objects. Matrix Lie groups naturally arise in • – geometry (classical, algebraic, differential) – complex analyis – differential equations – Fourier analysis – algebra (group theory, ring theory) – number theory – combinatorics. RhodesUniv CCR 2 Maths Seminar 2007 Matrix Lie groups are encountered in many • applications in – physics (geometric mechanics, quantum con- trol) – engineering (motion control, robotics) – computational chemistry (molecular mo- tion) – computer science (computer animation, computer vision, quantum computation). “It turns out that matrix [Lie] groups • pop up in virtually any investigation of objects with symmetries, such as molecules in chemistry, particles in physics, and projective spaces in geometry”. (K. Tapp, 2005) RhodesUniv CCR 3 Maths Seminar 2007 EXAMPLE 1 : The Euclidean group E (2). • E (2) = F : R2 R2 F is an isometry . → | n o The vector space R2 is equipped with the standard Euclidean structure (the “dot product”) x y = x y + x y (x, y R2), • 1 1 2 2 ∈ hence with the Euclidean distance d (x, y) = (y x) (y x) (x, y R2). -
The Exponential Function for Matrices
The exponential function for matrices Matrix exponentials provide a concise way of describing the solutions to systems of homoge- neous linear differential equations that parallels the use of ordinary exponentials to solve simple differential equations of the form y0 = λ y. For square matrices the exponential function can be defined by the same sort of infinite series used in calculus courses, but some work is needed in order to justify the construction of such an infinite sum. Therefore we begin with some material needed to prove that certain infinite sums of matrices can be defined in a mathematically sound manner and have reasonable properties. Limits and infinite series of matrices Limits of vector valued sequences in Rn can be defined and manipulated much like limits of scalar valued sequences, the key adjustment being that distances between real numbers that are expressed in the form js−tj are replaced by distances between vectors expressed in the form jx−yj. 1 Similarly, one can talk about convergence of a vector valued infinite series n=0 vn in terms of n the convergence of the sequence of partial sums sn = i=0 vk. As in the case of ordinary infinite series, the best form of convergence is absolute convergence, which correspondsP to the convergence 1 P of the real valued infinite series jvnj with nonnegative terms. A fundamental theorem states n=0 1 that a vector valued infinite series converges if the auxiliary series jvnj does, and there is P n=0 a generalization of the standard M-test: If jvnj ≤ Mn for all n where Mn converges, then P n vn also converges. -
Approximating the Exponential from a Lie Algebra to a Lie Group
MATHEMATICS OF COMPUTATION Volume 69, Number 232, Pages 1457{1480 S 0025-5718(00)01223-0 Article electronically published on March 15, 2000 APPROXIMATING THE EXPONENTIAL FROM A LIE ALGEBRA TO A LIE GROUP ELENA CELLEDONI AND ARIEH ISERLES 0 Abstract. Consider a differential equation y = A(t; y)y; y(0) = y0 with + y0 2 GandA : R × G ! g,whereg is a Lie algebra of the matricial Lie group G. Every B 2 g canbemappedtoGbythematrixexponentialmap exp (tB)witht 2 R. Most numerical methods for solving ordinary differential equations (ODEs) on Lie groups are based on the idea of representing the approximation yn of + the exact solution y(tn), tn 2 R , by means of exact exponentials of suitable elements of the Lie algebra, applied to the initial value y0. This ensures that yn 2 G. When the exponential is difficult to compute exactly, as is the case when the dimension is large, an approximation of exp (tB) plays an important role in the numerical solution of ODEs on Lie groups. In some cases rational or poly- nomial approximants are unsuitable and we consider alternative techniques, whereby exp (tB) is approximated by a product of simpler exponentials. In this paper we present some ideas based on the use of the Strang splitting for the approximation of matrix exponentials. Several cases of g and G are considered, in tandem with general theory. Order conditions are discussed, and a number of numerical experiments conclude the paper. 1. Introduction Consider the differential equation (1.1) y0 = A(t; y)y; y(0) 2 G; with A : R+ × G ! g; where G is a matricial Lie group and g is the underlying Lie algebra. -
LIE GROUPS and ALGEBRAS NOTES Contents 1. Definitions 2
LIE GROUPS AND ALGEBRAS NOTES STANISLAV ATANASOV Contents 1. Definitions 2 1.1. Root systems, Weyl groups and Weyl chambers3 1.2. Cartan matrices and Dynkin diagrams4 1.3. Weights 5 1.4. Lie group and Lie algebra correspondence5 2. Basic results about Lie algebras7 2.1. General 7 2.2. Root system 7 2.3. Classification of semisimple Lie algebras8 3. Highest weight modules9 3.1. Universal enveloping algebra9 3.2. Weights and maximal vectors9 4. Compact Lie groups 10 4.1. Peter-Weyl theorem 10 4.2. Maximal tori 11 4.3. Symmetric spaces 11 4.4. Compact Lie algebras 12 4.5. Weyl's theorem 12 5. Semisimple Lie groups 13 5.1. Semisimple Lie algebras 13 5.2. Parabolic subalgebras. 14 5.3. Semisimple Lie groups 14 6. Reductive Lie groups 16 6.1. Reductive Lie algebras 16 6.2. Definition of reductive Lie group 16 6.3. Decompositions 18 6.4. The structure of M = ZK (a0) 18 6.5. Parabolic Subgroups 19 7. Functional analysis on Lie groups 21 7.1. Decomposition of the Haar measure 21 7.2. Reductive groups and parabolic subgroups 21 7.3. Weyl integration formula 22 8. Linear algebraic groups and their representation theory 23 8.1. Linear algebraic groups 23 8.2. Reductive and semisimple groups 24 8.3. Parabolic and Borel subgroups 25 8.4. Decompositions 27 Date: October, 2018. These notes compile results from multiple sources, mostly [1,2]. All mistakes are mine. 1 2 STANISLAV ATANASOV 1. Definitions Let g be a Lie algebra over algebraically closed field F of characteristic 0. -
Unipotent Flows and Applications
Clay Mathematics Proceedings Volume 10, 2010 Unipotent Flows and Applications Alex Eskin 1. General introduction 1.1. Values of indefinite quadratic forms at integral points. The Op- penheim Conjecture. Let X Q(x1; : : : ; xn) = aijxixj 1≤i≤j≤n be a quadratic form in n variables. We always assume that Q is indefinite so that (so that there exists p with 1 ≤ p < n so that after a linear change of variables, Q can be expresses as: Xp Xn ∗ 2 − 2 Qp(y1; : : : ; yn) = yi yi i=1 i=p+1 We should think of the coefficients aij of Q as real numbers (not necessarily rational or integer). One can still ask what will happen if one substitutes integers for the xi. It is easy to see that if Q is a multiple of a form with rational coefficients, then the set of values Q(Zn) is a discrete subset of R. Much deeper is the following conjecture: Conjecture 1.1 (Oppenheim, 1929). Suppose Q is not proportional to a ra- tional form and n ≥ 5. Then Q(Zn) is dense in the real line. This conjecture was extended by Davenport to n ≥ 3. Theorem 1.2 (Margulis, 1986). The Oppenheim Conjecture is true as long as n ≥ 3. Thus, if n ≥ 3 and Q is not proportional to a rational form, then Q(Zn) is dense in R. This theorem is a triumph of ergodic theory. Before Margulis, the Oppenheim Conjecture was attacked by analytic number theory methods. (In particular it was known for n ≥ 21, and for diagonal forms with n ≥ 5). -
Singles out a Specific Basis
Quantum Information and Quantum Noise Gabriel T. Landi University of Sao˜ Paulo July 3, 2018 Contents 1 Review of quantum mechanics1 1.1 Hilbert spaces and states........................2 1.2 Qubits and Bloch’s sphere.......................3 1.3 Outer product and completeness....................5 1.4 Operators................................7 1.5 Eigenvalues and eigenvectors......................8 1.6 Unitary matrices.............................9 1.7 Projective measurements and expectation values............ 10 1.8 Pauli matrices.............................. 11 1.9 General two-level systems....................... 13 1.10 Functions of operators......................... 14 1.11 The Trace................................ 17 1.12 Schrodinger’s¨ equation......................... 18 1.13 The Schrodinger¨ Lagrangian...................... 20 2 Density matrices and composite systems 24 2.1 The density matrix........................... 24 2.2 Bloch’s sphere and coherence...................... 29 2.3 Composite systems and the almighty kron............... 32 2.4 Entanglement.............................. 35 2.5 Mixed states and entanglement..................... 37 2.6 The partial trace............................. 39 2.7 Reduced density matrices........................ 42 2.8 Singular value and Schmidt decompositions.............. 44 2.9 Entropy and mutual information.................... 50 2.10 Generalized measurements and POVMs................ 62 3 Continuous variables 68 3.1 Creation and annihilation operators................... 68 3.2 Some important -
Contents 1 Root Systems
Stefan Dawydiak February 19, 2021 Marginalia about roots These notes are an attempt to maintain a overview collection of facts about and relationships between some situations in which root systems and root data appear. They also serve to track some common identifications and choices. The references include some helpful lecture notes with more examples. The author of these notes learned this material from courses taught by Zinovy Reichstein, Joel Kam- nitzer, James Arthur, and Florian Herzig, as well as many student talks, and lecture notes by Ivan Loseu. These notes are simply collected marginalia for those references. Any errors introduced, especially of viewpoint, are the author's own. The author of these notes would be grateful for their communication to [email protected]. Contents 1 Root systems 1 1.1 Root space decomposition . .2 1.2 Roots, coroots, and reflections . .3 1.2.1 Abstract root systems . .7 1.2.2 Coroots, fundamental weights and Cartan matrices . .7 1.2.3 Roots vs weights . .9 1.2.4 Roots at the group level . .9 1.3 The Weyl group . 10 1.3.1 Weyl Chambers . 11 1.3.2 The Weyl group as a subquotient for compact Lie groups . 13 1.3.3 The Weyl group as a subquotient for noncompact Lie groups . 13 2 Root data 16 2.1 Root data . 16 2.2 The Langlands dual group . 17 2.3 The flag variety . 18 2.3.1 Bruhat decomposition revisited . 18 2.3.2 Schubert cells . 19 3 Adelic groups 20 3.1 Weyl sets . 20 References 21 1 Root systems The following examples are taken mostly from [8] where they are stated without most of the calculations. -
Matrix Exponential Updates for On-Line Learning and Bregman
Matrix Exponential Updates for On-line Learning and Bregman Projection ¥ ¤£ Koji Tsuda ¢¡, Gunnar Ratsch¨ and Manfred K. Warmuth Max Planck Institute for Biological Cybernetics Spemannstr. 38, 72076 Tubingen,¨ Germany ¡ AIST CBRC, 2-43 Aomi, Koto-ku, Tokyo, 135-0064, Japan £ Fraunhofer FIRST, Kekulestr´ . 7, 12489 Berlin, Germany ¥ University of Calif. at Santa Cruz ¦ koji.tsuda,gunnar.raetsch § @tuebingen.mpg.de, [email protected] Abstract We address the problem of learning a positive definite matrix from exam- ples. The central issue is to design parameter updates that preserve pos- itive definiteness. We introduce an update based on matrix exponentials which can be used as an on-line algorithm or for the purpose of finding a positive definite matrix that satisfies linear constraints. We derive this update using the von Neumann divergence and then use this divergence as a measure of progress for proving relative loss bounds. In experi- ments, we apply our algorithms to learn a kernel matrix from distance measurements. 1 Introduction Most learning algorithms have been developed to learn a vector of parameters from data. However, an increasing number of papers are now dealing with more structured parameters. More specifically, when learning a similarity or a distance function among objects, the parameters are defined as a positive definite matrix that serves as a kernel (e.g. [12, 9, 11]). Learning is typically formulated as a parameter updating procedure to optimize a loss function. The gradient descent update [5] is one of the most commonly used algorithms, but it is not appropriate when the parameters form a positive definite matrix, because the updated parameter is not necessarily positive definite. -
Matrix Exponentiated Gradient Updates for On-Line Learning and Bregman Projection
JournalofMachineLearningResearch6(2005)995–1018 Submitted 11/04; Revised 4/05; Published 6/05 Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection Koji Tsuda [email protected] Max Planck Institute for Biological Cybernetics Spemannstrasse 38 72076 Tubingen,¨ Germany, and Computational Biology Research Center National Institute of Advanced Science and Technology (AIST) 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan Gunnar Ratsch¨ [email protected] Friedrich Miescher Laboratory of the Max Planck Society Spemannstrasse 35 72076 Tubingen,¨ Germany Manfred K. Warmuth [email protected] Computer Science Department University of California Santa Cruz, CA 95064, USA Editor: Yoram Singer Abstract We address the problem of learning a symmetric positive definite matrix. The central issue is to de- sign parameter updates that preserve positive definiteness. Our updates are motivated with the von Neumann divergence. Rather than treating the most general case, we focus on two key applications that exemplify our methods: on-line learning with a simple square loss, and finding a symmetric positive definite matrix subject to linear constraints. The updates generalize the exponentiated gra- dient (EG) update and AdaBoost, respectively: the parameter is now a symmetric positive definite matrix of trace one instead of a probability vector (which in this context is a diagonal positive def- inite matrix with trace one). The generalized updates use matrix logarithms and exponentials to preserve positive definiteness. Most importantly, we show how the derivation and the analyses of the original EG update and AdaBoost generalize to the non-diagonal case. We apply the resulting matrix exponentiated gradient (MEG) update and DefiniteBoost to the problem of learning a kernel matrix from distance measurements.