POSITIVE MAP AS DIFFERENCE OF TWO COMPLETELY POSITIVE OR SUPER-POSITIVE MAPS

Similar documents
ENTANGLED STATES ARISING FROM INDECOMPOSABLE POSITIVE LINEAR MAPS. 1. Introduction

Elementary linear algebra

SCHUR IDEALS AND HOMOMORPHISMS OF THE SEMIDEFINITE CONE

Duke University, Department of Electrical and Computer Engineering Optimization for Scientists and Engineers c Alex Bronstein, 2014

ALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA

Compression, Matrix Range and Completely Positive Map

Summer School on Quantum Information Science Taiyuan University of Technology

MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators.

Lecture notes on Quantum Computing. Chapter 1 Mathematical Background

Linear and Multilinear Algebra. Linear maps preserving rank of tensor products of matrices

Linear Algebra: Matrix Eigenvalue Problems

Clarkson Inequalities With Several Operators

Algebra C Numerical Linear Algebra Sample Exam Problems

Ir O D = D = ( ) Section 2.6 Example 1. (Bottom of page 119) dim(v ) = dim(l(v, W )) = dim(v ) dim(f ) = dim(v )

Boolean Inner-Product Spaces and Boolean Matrices

The following definition is fundamental.

Basic Concepts in Matrix Algebra

arxiv: v1 [quant-ph] 24 May 2011

Linear Algebra Massoud Malek

Positive and doubly stochastic maps, and majorization in Euclidean Jordan algebras

Linear Algebra and Matrix Inversion

MAT 445/ INTRODUCTION TO REPRESENTATION THEORY

ON THE HÖLDER CONTINUITY OF MATRIX FUNCTIONS FOR NORMAL MATRICES

08a. Operators on Hilbert spaces. 1. Boundedness, continuity, operator norms

A COMMENT ON FREE GROUP FACTORS

Intertibility and spectrum of the multiplication operator on the space of square-summable sequences

Linear Algebra Review. Vectors

Matrices and Vectors. Definition of Matrix. An MxN matrix A is a two-dimensional array of numbers A =

EXAM. Exam 1. Math 5316, Fall December 2, 2012

arxiv: v3 [math.ra] 22 Aug 2014

Review of Some Concepts from Linear Algebra: Part 2

The Jordan Canonical Form

Lecture notes: Applied linear algebra Part 1. Version 2

Maths for Signals and Systems Linear Algebra in Engineering

Characterization of half-radial matrices

Chapter 7. Canonical Forms. 7.1 Eigenvalues and Eigenvectors

Mathematical foundations - linear algebra

October 25, 2013 INNER PRODUCT SPACES

WOMP 2001: LINEAR ALGEBRA. 1. Vector spaces

Lecture 7: Positive Semidefinite Matrices

FACIAL STRUCTURES FOR SEPARABLE STATES

FINDING DECOMPOSITIONS OF A CLASS OF SEPARABLE STATES

ON MATRIX VALUED SQUARE INTEGRABLE POSITIVE DEFINITE FUNCTIONS

Linear Algebra (Review) Volker Tresp 2017

A CHARACTERIZATION OF THE OPERATOR-VALUED TRIANGLE EQUALITY

CHAPTER VIII HILBERT SPACES

Mathematical Methods wk 2: Linear Operators

Matrix representation of a linear map

Review 1 Math 321: Linear Algebra Spring 2010

arxiv: v1 [quant-ph] 4 Jul 2013

Dual Spaces. René van Hassel

Math 102, Winter Final Exam Review. Chapter 1. Matrices and Gaussian Elimination

A 3 3 DILATION COUNTEREXAMPLE

PRODUCT OF OPERATORS AND NUMERICAL RANGE

LINEAR ALGEBRA BOOT CAMP WEEK 4: THE SPECTRAL THEOREM

Some results on the reverse order law in rings with involution

Math Camp II. Basic Linear Algebra. Yiqing Xu. Aug 26, 2014 MIT

Matrix Algebra: Definitions and Basic Operations

Linear Algebra (Review) Volker Tresp 2018

Contents. Preface for the Instructor. Preface for the Student. xvii. Acknowledgments. 1 Vector Spaces 1 1.A R n and C n 2

MULTICENTRIC HOLOMORPHIC CALCULUS FOR n TUPLES OF COMMUTING OPERATORS

On positive maps in quantum information.

Linear Operators Preserving the Numerical Range (Radius) on Triangular Matrices

Strict diagonal dominance and a Geršgorin type theorem in Euclidean

Optimization Theory. A Concise Introduction. Jiongmin Yong

Abstract. In this article, several matrix norm inequalities are proved by making use of the Hiroshima 2003 result on majorization relations.

Preliminaries on von Neumann algebras and operator spaces. Magdalena Musat University of Copenhagen. Copenhagen, January 25, 2010

Various notions of positivity for bi-linear maps and applications to tri-partite entanglement

Matrix Algebra: Summary

Math Camp Lecture 4: Linear Algebra. Xiao Yu Wang. Aug 2010 MIT. Xiao Yu Wang (MIT) Math Camp /10 1 / 88

ELA MAPS PRESERVING GENERAL MEANS OF POSITIVE OPERATORS

Elementary Row Operations on Matrices

Permutation invariant proper polyhedral cones and their Lyapunov rank

LINEAR ALGEBRA REVIEW

Elementary maths for GMT

HONORS LINEAR ALGEBRA (MATH V 2020) SPRING 2013

Properties of Matrices and Operations on Matrices

Jim Lambers MAT 610 Summer Session Lecture 1 Notes

Linear Algebra Done Wrong. Sergei Treil. Department of Mathematics, Brown University

Math 4377/6308 Advanced Linear Algebra

Chap 3. Linear Algebra

Chapter 1 Vector Spaces

Review of Basic Concepts in Linear Algebra

Linear Algebra M1 - FIB. Contents: 5. Matrices, systems of linear equations and determinants 6. Vector space 7. Linear maps 8.

Paradigms of Probabilistic Modelling

Math 121 Homework 4: Notes on Selected Problems

Banach Journal of Mathematical Analysis ISSN: (electronic)

b jσ(j), Keywords: Decomposable numerical range, principal character AMS Subject Classification: 15A60

Math Bootcamp An p-dimensional vector is p numbers put together. Written as. x 1 x =. x p

Knowledge Discovery and Data Mining 1 (VO) ( )

1 Linear Algebra Problems

j=1 x j p, if 1 p <, x i ξ : x i < ξ} 0 as p.

Economics 204 Summer/Fall 2010 Lecture 10 Friday August 6, 2010

ELEMENTARY LINEAR ALGEBRA

Lie Algebra of Unit Tangent Bundle in Minkowski 3-Space

Hilbert Space Methods Used in a First Course in Quantum Mechanics A Recap WHY ARE WE HERE? QUOTE FROM WIKIPEDIA

Quantum Computing Lecture 2. Review of Linear Algebra

Summer School: Semidefinite Optimization

Categories and Quantum Informatics: Hilbert spaces

Linear algebra 2. Yoav Zemel. March 1, 2012

Transcription:

Adv. Oper. Theory 3 (2018), no. 1, 53 60 http://doi.org/10.22034/aot.1702-1129 ISSN: 2538-225X (electronic) http://aot-math.org POSITIVE MAP AS DIFFERENCE OF TWO COMPLETELY POSITIVE OR SUPER-POSITIVE MAPS TSUYOSHI ANDO This paper is dedicated to the memory of the late Professor Uffe Haagerup Communicated by M. Tomforde Abstract. For a linear map from M m to M n, besides the usual positivity, there are two stronger notions, complete positivity and super-positivity. Given a positive linear map ϕ we study a decomposition ϕ = ϕ (1) ϕ (2) with completely positive linear maps ϕ (j) (j = 1, 2). Here ϕ (1) + ϕ (2) is of simple form with norm small as possible. The same problem is discussed with superpositivity in place of complete positivity. 1. Introduction and problems Let M k denote the space of k k (complex) matrices. Each matrix in M k is considered as a linear map from C k to itself. An element x of C k is treated as a column k-vector, correspondingly x is a row k-vector. Then given a, b C k, according to the rule of matrix multiplication, a b is the inner product of a and b, that is, a b = a b while ba is a matrix of rank-one in M k. Be careful about that the inner product is linear in b and anti-linear in a. For selfadjoint X, Y M k, the order relation X Y or equivalently Y X is defined as X Y is positive semi-definite. Therefore X 0 or 0 X simply means that X is positive semi-definite. The norm X denotes the operator norm X := sup Xa. a =1 Copyright 2016 by the Tusi Mathematical Research Group. Date: Received: Feb. 25, 2017; Accepted: Mar. 11, 2017. 2010 Mathematics Subject Classification. Primary 47C15; Secondary 47A30, 15A69. Key words and phrases. Positive map, completely positive map, super-positive map, norm, tensor product. 53

54 T. ANDO Throughout this paper, we assume 2 m n. There are canonical identifications: M m M n M m (M n ) M mn. Here M m (M n ) denotes the space of m m block-matrices with entries in M n and the first identification is in the following way: X Y [ξ jk Y ] j,k for X = [ξ jk ] j,k M m, Y M n. Here, for simplicity of notations, an m m (numerical) matrix with (j, k)-entry ξ j,k is written as [ξ jk ] j,k. In analogy, an m m block-matrix with (j, k)-block-entry S j,k is denoted by [S jk ] j,k. Therefore a block matrix S = [S jk ] j,k M m (M n ) is uniquely assigned as [S jk ] j,k j,k E jk S jk, where E jk (j, k = 1, 2,..., m) are matrix-units in M m, that is, E jk = e j e k where e j (j = 1,..., m) is the canonical orthonormal basis of C m. In the following, M (m,n) denotes the real subspace of M m (M n ), consisting of selfadjoint elements, that is, the subspace of S = [S jk ] j,k with S jk = S kj (j, k = 1,..., m). The cone of positive semi-definite (block) matrices in M (m,n) will be denoted by P 0. The order relation based on this cone is denoted by as usual. Therefore S 0 means that S is positive semi-definite. In the tensor product theory a fact of key importance is the following (see [3, Chapter I-4]): 0 X M m, 0 Y M n = 0 X Y. The cone generated by X Y with 0 X M m and 0 Y M n will be denoted by P +. Because of finite dimensionality of M (m,n) it is known (see [2, p.8]) that P + is a (topologically) closed cone, contained in P 0. A (block) matrix in P + is said to be separable. The space M (m,n) becomes a real Hilbert space with inner product T S := Tr(TS), and we can consider the dual cone P of the cone P + defined by S P S T 0 T P +. (1.1) The cone P is (topologically) closed by definition. In view of the closedness of P +, according to a general theory of convexity, P + is the dual cone of P. It is well-known that the cone P 0 is selfdual, that is, S P 0 S T 0 T P 0. As a consequence we have the inclusion relations: Notice the algebraic relations: P + P 0 P. P 0 P 0 = P + P + = M (m,n). (1.2)

POSITIVE MAP AS DIFFERENCE OF TWO MAPS 55 Given a linear map ϕ : M m M n, its Choi matrix C ϕ [7, p.49] is defined by On the basis of the relation ϕ(x) = j,k C ϕ := [ϕ(e jk )] j,k M m (M n ). ξ jk ϕ(e jk ) X = [ξ jk ] j,k M m, the original map ϕ is uniquely recaptured from its Choi matrix. Further ϕ C ϕ is a linear bijection between the space of selfadjoint linear maps ϕ, that is, ϕ(x ) = ϕ(x) X M m, and the space M (m,n). This bijection is usually called the Jamiolkowski isomorphism (see [7, p.49]). A linear map ϕ : M m M n is said to be positive if ϕ(x) 0 whenever X 0. Our starting point is the following relation, deduced from (1.1) and the definition of P + (see [2, Theorem 2.1]): ϕ positive C ϕ P (1.3) x S jk x 0 in M m x C n. j,k There is a welll-known notion, stronger than positivity. A linear map ϕ : M m M n is said to be completely positive if the linear map id N ϕ : M N M m M N (M m ) M N (M n ) defined by (id N ϕ)([t jk ] j,k ) := [ϕ(t jk )] j,k T jk M N is positive for all N = 1, 2,.... Usefulness of use of the Choi matrix is seen in the following theorem of Choi [4] (see [2, Theorem 2.2]) ϕ completely positive C ϕ P 0. (1.4) In accordance with (1.3) and (1.4), a positive linear map ϕ : M m M n will be said to be super-positive [2, p.11] when C ϕ P +. (1.5) Therefore a positive linear map ϕ is completely positive if and only if all eigenvalues of its Choi matrix are non-negative. On the contrary, there is no simple test to check super-positivity of ϕ. An obvious condition, which guarantees its super-positivity, is block-diagonality of the Choi matrix C ϕ = [S jk ] j,k, that is, S jk = 0 for j k. In this case S jj 0 (j = 1,..., m) is guaranteed by the positivity of ϕ. Though not used in the subsequent discussion, we notice that the following intrinsic characterization of super-positivity of ϕ was established by Horodecki s [5, Theorem 2] ϕ super positive ψ ϕ completely positive positive ψ : M n M N.

56 T. ANDO As usual, the (mapping) norm of a linear map ϕ : M m M n is defined by ϕ = sup{ ϕ(x) ; X 1, X M m }. Here advantage of positivity of ϕ is seen in the following fact, a consequence of a theorem of Russo-Dye [6] (see [7, Theorem 1.3.3]): ϕ positive = ϕ = ϕ(i m ). (1.6) In view of (1.2), it is seen from (1.4) and (1.5) that every selfadjoint linear map ϕ : M m M n is written as difference of two completely positive (or even super-positive) linear maps ϕ (j) (j = 1, 2); ϕ = ϕ (1) ϕ (2). (1.7) Of course, such decomposition is never unique. In this paper, which is a continuation of [2], we study the problem how to construct a decomposition (1.7) of positive ϕ, for which the Choi matrix of ϕ (1) + ϕ (2) is block-diagonal and its norm is small as possible. 2. Case of complete positivity For notational convenience, let us define the partial trace χ(s) of S = [S jk ] j,k M (m,n) by χ(s) := j S jj M n. Then (1.6) says that ϕ positive = ϕ = χ(c ϕ ). (2.1) For selfadjoint S, its modulus S P 0 is defined as the positive (semi-definite) square root of S 2. Further its positive part S + and the negative part S are defined as S + := 1 { S + S} and 2 S := 1 { S S}. 2 All S, S + and S belong to the cone P 0 and the decomposition S = S + S is called the Jordan decomposition of S. (See [3, p. 99].) Lemma 2.1. If ϕ is a selfadjoint linear map : M m M n with Choi matrix C ϕ, A proof is found in [2, Theorem 6.2]. χ( C ϕ ) m ϕ. Theorem 2.2. Let ϕ be a selfadjoint linear map : M m M n with Choi matrix C ϕ. Define completely positive linear maps ϕ (1) and ϕ (2) by C ϕ (1) := C + ϕ and C ϕ (2) := C ϕ. Then ϕ = ϕ (1) ϕ (2) and ϕ (1) + ϕ (2) m ϕ.

Proof. By (2.1) and Lemma 2.1 POSITIVE MAP AS DIFFERENCE OF TWO MAPS 57 ϕ (1) + ϕ (2) = χ( C ϕ ) m ϕ. When ϕ is positive, a decomposition (1.7) with completely positive ϕ (1) and ϕ (2), for which C ϕ (1) + C ϕ (2) is block-diagonal and can be constructed rather easily. ϕ (1) (I m ) + ϕ (2) (I m ) = m ϕ(i m ), We need a result in M (2,n) for its proof. Lemma 2.3. A B X ±B B P C = ±B P Y 0 X, Y 0, X + Y = A + C. A B Proof. By (1.3), B P C means that A, C 0 and which implies that x Ax x Cx x Bx 2 x C n, x 1 2 (A + C)x x Bx x Cn. (2.2) We may assume here that A + C is invertible. Then, with D := { 1 2 (A + C)} 1 2, (2.2) means that the numerical radius of D 1 BD 1 is 1, that is, x 2 x (D 1 BD 1 )x x C n. Then by [1, Theorem 1] there are R, T 0 such that R + T = 2I n and [ ] R ±D 1 BD 1 ±D 1 B D 1 0. T Let X := DRD and Y := DT D. Then X + Y = A + C and X ±B ±B 0. Y To apply some results of M (2,n) to the case of M (m,n) the following trivial facts will be used without any mention. (1) A j 0 (j = 1, 2,..., m) = diag(a 1,..., A m ) P + P 0. Spp S (2) S = [S jk ] j,k P = pq P S qp S (in M (2,n) ) p < q. qq

58 T. ANDO A B (3) If B P C 0 (resp. P + ) in M (2,n) then, for any 1 j < k m, the (block) matrix S P 0 (resp. P + ) where S jj = A, S jk = B, S kj = B, S kk = C, and S pq = 0 if p j or q k. Theorem 2.4. Let ϕ be a positive linear map : M m M n. Then there are completely positive linear maps ϕ (j) (j = 1, 2) : M m M n such that ϕ = ϕ (1) ϕ (2), the Choi matrix of ϕ (1) + ϕ (2) is block-diagonal and ϕ (1) (I m ) + ϕ (2) (I m ) = m ϕ(i m ). Proof. Let C ϕ = [S jk ] j,k be the Choi matrix of ϕ. Since Sjj S jk P S kj S in M (2,n) j < k kk by Lemma 2.3 there are 0 X j,k, X k,j M n such that Xj,k ±S X j,k + X k,j = S jj + S kk and jk 0. (2.3) ±S kj X k,j Let ϕ (j) (j = 1, 2) be the selfadjoint linear maps: M m M n with respective Choi matrix C ϕ (j) (j = 1, 2) given by } C ϕ (1) := {Diag(C 1 2 ϕ ) + diag(a 1,..., A m ) + C ϕ and where and C ϕ (2) := 1 2 A j := {Diag(C ϕ ) + diag(a 1,..., A m ) C ϕ }, Diag(C ϕ ) := diag(s 11,..., S mm ) 1 k<j X k,j + j<k m X j,k (j = 1, 2,..., m). Then it is clear that ϕ = ϕ (1) ϕ (2), and by (2.3) ( ) χ C ϕ (1) + C ϕ (2) = m χ(c ϕ ), and that the Choi matrix of ϕ (1) + ϕ (2) is block-diagonal. That C ϕ (j) P 0 (j = 1, 2) comes also from (2.3). Therefore both ϕ (j) (j = 1, 2) are completely positive by (1.4). Optimality of the constant m in Theorem 2.4 is pointed out in [2, p.28]. In fact, when m = n, for the positive linear map ϕ 0 (X) := X T (transpose map) any decomposition ϕ 0 = ϕ (1) ϕ (2) with completely positive ϕ (j) (j = 1, 2) satisfies necessarily ϕ (1) + ϕ (2) m ϕ 0.

POSITIVE MAP AS DIFFERENCE OF TWO MAPS 59 3. Case of super-positivity In the case of decomposition with super-positive linear maps, there is no canonical decomposition as Jordan decomposition in Section 2. However, the same idea as in the proof of Theorem 2.4 can be used to find a suitable decomposition. This approach was used already in [2, Theorem 7.4]. Let me present the same result again to show how the difference of scalars, m and 2m 1, appears. Lemma 3.1. A B B P C = A + C ±B ±B P A + C +. A proof is found in [2, Theorem 4.10]. This lemma corresponds to Lemma 2.3. Theorem 3.2. Let ϕ be a positive linear map : M m M n. Then there are super-positive linear maps ϕ (j) (j = 1, 2) : M m M n such that ϕ = ϕ (1) ϕ (2), the Choi matrix of ϕ (1) + ϕ (2) is block-diagonal and ϕ (1) (I m ) + ϕ (2) (I m ) = (2m 1) ϕ(i m ). Proof. Let C ϕ = [S jk ] j,k be the Choi matrix of ϕ, and let ϕ (j) (j = 1, 2) be the linear maps with respective Choi matrix C ϕ (j) (j = 1, 2) given by } C ϕ (1) := {(m 1 1) Diag(C 2 ϕ ) + I m χ(c ϕ ) + C ϕ and C ϕ ( 2) := 1 2 {(m 1) Diag(C ϕ ) + I m χ(c ϕ ) C ϕ }. It is clear that ( ) χ C ϕ (1) + C ϕ (2) = (2m 1) χ(c ϕ ), and that the Choi matrix of ϕ (1) + ϕ (2) is block-diagonal. It remains to show that C ϕ (j) P + (j = 1, 2). As in the proof of Theorem 2.4, this follows principally from Lemma 3.1: Sjj + S kk ±S jk P ±S kj S jj + S + j < k. kk Optimality of the constant 2m 1 in Theorem 3.2 is pointed out in [2, Theorem 7.6]. In fact, when m = n, for the (completely) positive map ϕ 0 (X) = X (identity map), any decomposition ϕ 0 = ϕ (1) ϕ (2) with super-positive ϕ (j) (j = 1, 2) satisfies necessarily ϕ (1) + ϕ (2) (2m 1) ϕ 0.

60 T. ANDO References 1. T. Ando, Structure of operators with numerical radius one, Acta Sci. Math. (Szeged), 34 (1972), 169 178. 2. T. Ando, Cones and norms in the tensor product of matrix spaces, Linear Algebra Appl., 379 (2004), 3 41. 3. R. Bhatia, Matrix Analysis, Springer, New York, 1997. 4. M.-D. Choi, Completely positive linear map on complex matrices, Linear Algebra Appl. 10 (1975), 285 290. 5. M. Horodecki, P. Horodecki, and R. Horodecki, Separability of mixed states: necessary and sufficient conditions, Phys. Lett. A 233 (1996), no. 1-2, 1 8. 6. B. Russo and H. A. Dye, A note on unitary operators in C -algebra, Duke Math. J. 33 (1966), 413 416. 7. E. Størmer, Positive Linear Maps of Operator Algebras, Springer, New York, 2013. Hokkaido University (Emeritus), Japan. E-mail address: ando@es.hokudai.ac.jp