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On Quaslfree States of CAR and Bogoliubov Automorphisms
Publ. RIMS Kyoto Univ. Vol. 6 (1970/71), 385-442 On Quaslfree States of CAR and Bogoliubov Automorphisms By Huzihiro ARAKI Abstract A necessary and sufficient condition for two quasifree states of CAR to be quasiequivalent is obtained. Quasif ree states is characterized as the unique KMS state of a Bogoliubov automorphism of CAR. The structure of the group of all inner Bogoliubov automorphisms of CAR is clarified. §1. Introduction A classification of gauge invariant quasifree states of the canonical anticommutation relations (CAR) up to quasi and unitary equivalence is recently obtained by Powers and St0rmer [12]. We shall generalize their result to arbitrary quasifree states. We use the formalism developped earlier [2] and study quasifree state <ps of a self dual CAR algebra. It is then shown that <pSi and <pS2 are quasiequivalent if and only if Sllz — Sllz is in the Hiibert Schmidt class. For a gauge invariant quasifree state <pA in the paper of Powers and St0rmer, S = A@(1 — A) and hence our result is a direct generali- zation of Powers and St0rmer. The quasifree primary state <ps for which 5 does not have eigen- value 1 is shown to be the unique KMS state for the one parameter group r(£/GO) of Bogoiiubov * automorphisms of CAR, where r(t/GO) corresponds to a unitary transformation U(£)=ex.piAH of the direct sum of testing function spaces of creation and annihilation operators and H is related to 5 by S= (l + e"*)"1. This is used to simplify some of arguments. A quasifree state q>s is primary unless 1/2 is an isolated point spectrum of 5, has an odd multiplicity and S(l — S1) is in the Received May 6, 1970 and in revised form November 17, 1970. -
Unitary and Anti-Unitary Quantum Descriptions of the Classical Not Gate
G. Cattaneo, · G. Conte, · R. Leporini Unitary and Anti-Unitary Quantum Descriptions of the Classical Not Gate Abstract Two possible quantum descriptions of the classical Not gate are investigated in the framework of the Hilbert space C2: the unitary and the anti{unitary operator realizations. The two cases are distinguished interpret- ing the unitary Not as a quantum realization of the classical gate which on a fixed orthogonal pair of unit vectors, realizing once for all the classical bits 0 and 1, produces the required transformations 0 ! 1 and 1 ! 0 (i.e., logical quantum Not). The anti{unitary Not is a quantum realization of a gate which acts as a classical Not on any pair of mutually orthogonal vectors, each of which is a potential realization of the classical bits (i.e., universal quantum Not). Although the latter is not completely positive, one can give an approximated unitary realization of the gate by appending an ancilla. Fi- nally, we consider the unitary and the anti{unitary operator realizations of two important genuine quantum gates that transform elements of the com- putational basis of C2 into its superpositions: the square root of the identity and the square root of the Not. Keywords Quantum gates, universal quantum Not, square root of the Not, square root of the identity. G. Cattaneo Dipartimento di Informatica, Sistemistica e Comunicazione, Universit`adegli Studi di Milano{Bicocca, Via Bicocca degli Arcimboldi 8 Milano, 20126, Italy E-mail: [email protected] G. Conte Dipartimento di Filosofia, Universit`adegli Studi di Milano, Via Festa del Perdono 3 Milano, 20126, Italy E-mail: [email protected] R. -
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. -
On Some Extensions of Bernstein's Inequality for Self-Adjoint Operators
On Some Extensions of Bernstein's Inequality for Self-Adjoint Operators Stanislav Minsker∗ e-mail: [email protected] Abstract: We present some extensions of Bernstein's concentration inequality for random matrices. This inequality has become a useful and powerful tool for many problems in statis- tics, signal processing and theoretical computer science. The main feature of our bounds is that, unlike the majority of previous related results, they do not depend on the dimension d of the ambient space. Instead, the dimension factor is replaced by the “effective rank" associated with the underlying distribution that is bounded from above by d. In particular, this makes an extension to the infinite-dimensional setting possible. Our inequalities refine earlier results in this direction obtained by D. Hsu, S. M. Kakade and T. Zhang. Keywords and phrases: Bernstein's inequality, Random Matrix, Effective rank, Concen- tration inequality, Large deviations. 1. Introduction Theoretical analysis of many problems in applied mathematics can often be reduced to problems about random matrices. Examples include numerical linear algebra (randomized matrix decom- positions, approximate matrix multiplication), statistics and machine learning (low-rank matrix recovery, covariance estimation), mathematical signal processing, among others. n P Often, resulting questions can be reduced to estimates for the expectation E Xi − EXi or i=1 n P n probability P Xi − EXi > t , where fXigi=1 is a finite sequence of random matrices and i=1 k · k is the operator norm. Some of the early work on the subject includes the papers on noncom- mutative Khintchine inequalities [LPP91, LP86]; these tools were used by M. -
MATRICES WHOSE HERMITIAN PART IS POSITIVE DEFINITE Thesis by Charles Royal Johnson in Partial Fulfillment of the Requirements Fo
MATRICES WHOSE HERMITIAN PART IS POSITIVE DEFINITE Thesis by Charles Royal Johnson In Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy California Institute of Technology Pasadena, California 1972 (Submitted March 31, 1972) ii ACKNOWLEDGMENTS I am most thankful to my adviser Professor Olga Taus sky Todd for the inspiration she gave me during my graduate career as well as the painstaking time and effort she lent to this thesis. I am also particularly grateful to Professor Charles De Prima and Professor Ky Fan for the helpful discussions of my work which I had with them at various times. For their financial support of my graduate tenure I wish to thank the National Science Foundation and Ford Foundation as well as the California Institute of Technology. It has been important to me that Caltech has been a most pleasant place to work. I have enjoyed living with the men of Fleming House for two years, and in the Department of Mathematics the faculty members have always been generous with their time and the secretaries pleasant to work around. iii ABSTRACT We are concerned with the class Iln of n><n complex matrices A for which the Hermitian part H(A) = A2A * is positive definite. Various connections are established with other classes such as the stable, D-stable and dominant diagonal matrices. For instance it is proved that if there exist positive diagonal matrices D, E such that DAE is either row dominant or column dominant and has positive diag onal entries, then there is a positive diagonal F such that FA E Iln. -
ECE 487 Lecture 3 : Foundations of Quantum Mechanics II
ECE 487 Lecture 12 : Tools to Understand Nanotechnology III Class Outline: •Inverse Operator •Unitary Operators •Hermitian Operators Things you should know when you leave… Key Questions • What is an inverse operator? • What is the definition of a unitary operator? • What is a Hermetian operator and what do we use them for? M. J. Gilbert ECE 487 – Lecture 1 2 02/24/1 1 Tools to Understand Nanotechnology- III Let’s begin with a problem… Prove that the sum of the modulus squared of the matrix elements of a linear operator  is independent of the complete orthonormal basis used to represent the operator. M. J. Gilbert ECE 487 – Lecture 1 2 02/24/1 1 Tools to Understand Nanotechnology- III Last time we considered operators and how to represent them… •Most of the time we spent trying to understand the most simple of the operator classes we consider…the identity operator. •But he was only one of four operator classes to consider. 1. The identity operator…important for operator algebra. 2. Inverse operators…finding these often solves a physical problem mathematically and the are also important in operator algebra. 3. Unitary operators…these are very useful for changing the basis for representing the vectors and describing the evolution of quantum mechanical systems. 4. Hermitian operators…these are used to represent measurable quantities in quantum mechanics and they have some very powerful mathematical properties. M. J. Gilbert ECE 487 – Lecture 1 2 02/24/1 1 Tools to Understand Nanotechnology- III So, let’s move on to start discussing the inverse operator… Consider an operator  operating on some arbitrary function |f>. -
An Introduction to Functional Analysis for Science and Engineering David A
An introduction to functional analysis for science and engineering David A. B. Miller Stanford University This article is a tutorial introduction to the functional analysis mathematics needed in many physical problems, such as in waves in continuous media. This mathematics takes us beyond that of finite matrices, allowing us to work meaningfully with infinite sets of continuous functions. It resolves important issues, such as whether, why and how we can practically reduce such problems to finite matrix approximations. This branch of mathematics is well developed and its results are widely used in many areas of science and engineering. It is, however, difficult to find a readable introduction that both is deep enough to give a solid and reliable grounding but yet is efficient and comprehensible. To keep this introduction accessible and compact, I have selected only the topics necessary for the most important results, but the argument is mathematically complete and self-contained. Specifically, the article starts from elementary ideas of sets and sequences of real numbers. It then develops spaces of vectors or functions, introducing the concepts of norms and metrics that allow us to consider the idea of convergence of vectors and of functions. Adding the inner product, it introduces Hilbert spaces, and proceeds to the key forms of operators that map vectors or functions to other vectors or functions in the same or a different Hilbert space. This leads to the central concept of compact operators, which allows us to resolve many difficulties of working with infinite sets of vectors or functions. We then introduce Hilbert-Schmidt operators, which are compact operators encountered extensively in physical problems, such as those involving waves. -
216 Section 6.1 Chapter 6 Hermitian, Orthogonal, And
216 SECTION 6.1 CHAPTER 6 HERMITIAN, ORTHOGONAL, AND UNITARY OPERATORS In Chapter 4, we saw advantages in using bases consisting of eigenvectors of linear opera- tors in a number of applications. Chapter 5 illustrated the benefit of orthonormal bases. Unfortunately, eigenvectors of linear operators are not usually orthogonal, and vectors in an orthonormal basis are not likely to be eigenvectors of any pertinent linear operator. There are operators, however, for which eigenvectors are orthogonal, and hence it is possible to have a basis that is simultaneously orthonormal and consists of eigenvectors. This chapter introduces some of these operators. 6.1 Hermitian Operators § When the basis for an n-dimensional real, inner product space is orthonormal, the inner product of two vectors u and v can be calculated with formula 5.48. If v not only represents a vector, but also denotes its representation as a column matrix, we can write the inner product as the product of two matrices, one a row matrix and the other a column matrix, (u, v) = uT v. If A is an n n real matrix, the inner product of u and the vector Av is × (u, Av) = uT (Av) = (uT A)v = (AT u)T v = (AT u, v). (6.1) This result, (u, Av) = (AT u, v), (6.2) allows us to move the matrix A from the second term to the first term in the inner product, but it must be replaced by its transpose AT . A similar result can be derived for complex, inner product spaces. When A is a complex matrix, we can use equation 5.50 to write T T T (u, Av) = uT (Av) = (uT A)v = (AT u)T v = A u v = (A u, v). -
Bulk-Boundary Correspondence for Disordered Free-Fermion Topological
Bulk-boundary correspondence for disordered free-fermion topological phases Alexander Alldridge1, Christopher Max1,2, Martin R. Zirnbauer2 1 Mathematisches Institut, E-mail: [email protected] 2 Institut für theoretische Physik, E-mail: [email protected], [email protected] Received: 2019-07-12 Abstract: Guided by the many-particle quantum theory of interacting systems, we de- velop a uniform classification scheme for topological phases of disordered gapped free fermions, encompassing all symmetry classes of the Tenfold Way. We apply this scheme to give a mathematically rigorous proof of bulk-boundary correspondence. To that end, we construct real C∗-algebras harbouring the bulk and boundary data of disordered free-fermion ground states. These we connect by a natural bulk-to-boundary short ex- act sequence, realising the bulk system as a quotient of the half-space theory modulo boundary contributions. To every ground state, we attach two classes in different pic- tures of real operator K-theory (or KR-theory): a bulk class, using Van Daele’s picture, along with a boundary class, using Kasparov’s Fredholm picture. We then show that the connecting map for the bulk-to-boundary sequence maps these KR-theory classes to each other. This implies bulk-boundary correspondence, in the presence of disorder, for both the “strong” and the “weak” invariants. 1. Introduction The research field of topological quantum matter stands out by its considerable scope, pairing strong impact on experimental physics with mathematical structure and depth. On the experimental side, physicists are searching for novel materials with technolog- ical potential, e.g., for future quantum computers; on the theoretical side, mathemati- cians have been inspired to revisit and advance areas such as topological field theory and various types of generalised cohomology and homotopy theory. -
Nearly Tight Trotterization of Interacting Electrons
Nearly tight Trotterization of interacting electrons Yuan Su1,2, Hsin-Yuan Huang1,3, and Earl T. Campbell4 1Institute for Quantum Information and Matter, Caltech, Pasadena, CA 91125, USA 2Google Research, Venice, CA 90291, USA 3Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA 91125, USA 4AWS Center for Quantum Computing, Pasadena, CA 91125, USA We consider simulating quantum systems on digital quantum computers. We show that the performance of quantum simulation can be improved by simultaneously exploiting commutativity of the target Hamiltonian, sparsity of interactions, and prior knowledge of the initial state. We achieve this us- ing Trotterization for a class of interacting electrons that encompasses various physical systems, including the plane-wave-basis electronic structure and the Fermi-Hubbard model. We estimate the simulation error by taking the tran- sition amplitude of nested commutators of the Hamiltonian terms within the η-electron manifold. We develop multiple techniques for bounding the transi- tion amplitude and expectation of general fermionic operators, which may be 5/3 of independent interest. We show that it suffices to use n n4/3η2/3 no(1) η2/3 + gates to simulate electronic structure in the plane-wave basis with n spin or- bitals and η electrons, improving the best previous result in second quantiza- tion up to a negligible factor while outperforming the first-quantized simulation when n = η2−o(1). We also obtain an improvement for simulating the Fermi- Hubbard model. We construct concrete examples for which our bounds are almost saturated, giving a nearly tight Trotterization of interacting electrons. arXiv:2012.09194v2 [quant-ph] 30 Jun 2021 Accepted in Quantum 2021-06-03, click title to verify. -
On Sums of Range Symmetric Matrices in Minkowski Space
BULLETIN of the Bull. Malaysian Math. Sc. Soc. (Second Series) 25 (2002) 137-148 MALAYSIAN MATHEMATICAL SCIENCES SOCIETY On Sums of Range Symmetric Matrices in Minkowski Space AR. MEENAKSHI AND D. KRISHNASWAMY Department of Mathematics, Annamalai University, Annamalainagar - 608 002, Tamil Nadu, South India Abstract. We give necessary and sufficient condition for the sums of range symmetric matrices to be range symmetric in Minkowski space m. As an application it is shown that the sum and parallel sum of parallel summable range symmetric matrices are range symmetric. 1. Introduction . Throughout we shall deal with C n×n , the space of n× n complex matrices. Let C n be the space of complex n-tuples. We shall index the components of a complex vector in n C from 0 to n − 1. That is u = (u0 , u1, u2 , L, un−1 ). Let G be the Minkowski metric tensor defined by Gu = (u0 , − u1 , − u2 , L, − un−1 ). Clearly the Minkowski ⎡ 1 0 ⎤ 2 n metric matrix G = ⎢ ⎥ and G = I n . Minkowski inner product on C is ⎣ 0 − I n−1 ⎦ defined by (u, v) = < u, Gv > , where < ⋅ , ⋅ > denotes the conventional Hilbert space inner product. A space with Minkowski inner product is called a Minkowski space denoted as m. With respect to the Minkowski inner product the adjoint of a matrix A ∈ C n×n is given by A~ = GA∗ G , where A∗ is the usual Hermitian adjoint. Naturally we call a matrix A ∈ C n×n m-symmetric in Minkowski space if A~ = A, and m-orthogonal if A~ A = I . -
Complex Inner Product Spaces
Complex Inner Product Spaces A vector space V over the field C of complex numbers is called a (complex) inner product space if it is equipped with a pairing h, i : V × V → C which has the following properties: 1. (Sesqui-symmetry) For every v, w in V we have hw, vi = hv, wi. 2. (Linearity) For every u, v, w in V and a in C we have langleau + v, wi = ahu, wi + hv, wi. 3. (Positive definite-ness) For every v in V , we have hv, vi ≥ 0. Moreover, if hv, vi = 0, the v must be 0. Sometimes the third condition is replaced by the following weaker condition: 3’. (Non-degeneracy) For every v in V , there is a w in V so that hv, wi 6= 0. We will generally work with the stronger condition of positive definite-ness as is conventional. A basis f1, f2,... of V is called orthogonal if hfi, fji = 0 if i < j. An orthogonal basis of V is called an orthonormal (or unitary) basis if, in addition hfi, fii = 1. Given a linear transformation A : V → V and another linear transformation B : V → V , we say that B is the adjoint of A, if for all v, w in V , we have hAv, wi = hv, Bwi. By sesqui-symmetry we see that A is then the adjoint of B as well. Moreover, by positive definite-ness, we see that if A has an adjoint B, then this adjoint is unique. Note that, when V is infinite dimensional, we may not be able to find an adjoint for A! In case it does exist it is denoted as A∗.