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Robust Hadamard Matrices and Equi-Entangled Bases

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Robust Hadamard Matrices and Equi-Entangled Bases Robust Hadamard matrices and equi-entangled bases Grzegorz Rajchel1;2 Coauthors of the paper: Adam G ˛asiorowski2, Karol Zyczkowski˙ 1;3 1Centrum Fizyki Teoretycznej PAN; 2 Wydział Fizyki, Uniwersytet Warszawski; 3Instytut Fizyki im. Smoluchowskiego, Uniwersytet Jagiellonski´ Introduction Now we can introduce another notion important for results of this work: Definition 8 A symmetric matrix with entries ±1 outside the diagonal and 0 at the diagonal, which satisfy or- Hadamard matrices with particular structure attract a lot of attention, as their existence is related to several prob- thogonality relations: T lems in combinatorics and mathematical physics. Usually one poses a question, whether for a given size n there CC = (n − 1)I; exists a Hadamard matrix with a certain symmetry or satisfying some additional conditions. is called a symmetric conference matrix. We introduce notion of robust Hadamard matrices. This will be useful to broaden our understanding of the problem of unistochasticity inside the Birkhoff polytope. Lemma 9 Matrices of the structure: R H = C + iI; Birkhoff polytope where C is symmetric conference matrix, are complex robust Hadamard matrices. Definition 1 The set Bn of bistochastic matrices (also called Birkhoff polytope) consists of matrices with non- negative entries which satisfy two sum conditions for rows and columns: Main result n n We proved following propositions: X X Bn = fB 2 Mn(R); Bij = Bji = 1; j = 1; : : : ; ng (1) Proposition 10 For every order n, for which there exists a symmetric conference matrix or a skew Hadamard i=1 i=1 matrix, every matrix belonging to any ray R or any counter-ray Re of the Birkhoff polytope Bn is unistochastic. Definition 2 A bistochastic matrix B 2 Bn is called unistochastic if and only if there exists a unitary matrix n = 4k k < 69 ∗ Existence of skew Hadamard matrices for orders is proved for [3]. It is known that for dimen- U 2 U(n), UU = I, such that sions n = 6; 10; 14; 18 there exists a symmetric conference matrix [4]. 2 Those facts imply the main result of this work: Bij = jUijj ; i; j = 1; 2; : : : ; n (2) Theorem 11 For any even order n ≤ 20 all rays and counter-rays of the Birkhoff polytope Bn are The sets of all unistochastic matrices of order n will be denoted by U . n 1. orthostochastic (for n = 2; 4; 8; 12; 16; 20) or 2. unistochastic (for n = 6; 10; 14; 18). In search for unistochasticity It is easy to see that for n = 2 all three sets coincide, O2 = U2 = B2. While conditions for unistochasticity are Set U4 of unistochastic matrices of order n = 4 known [1] for n = 3, the case n ≥ 4 remains open – see [2]. Given an arbitrary bistochastic matrix B of order n, it is easy to verify whether certain necessary conditions for unistochasticity are satisfied, but in general, no Although analytic form of conditions for a matrix of order n = 4 to be unistochastic remains still not known, we universal sufficient conditions are known. shall apply a numerical procedure proposed by Haagerup to study the properties of the set U4 of unistochastic We call these relations as "chain" conditions: the longest link of closed chain cannot be longer than the sum of matrices of order n = 4. There exist different kinds of edges in B labeled by their length in sense of the Hilbert-Schmidt distance, all other links. p 4 D(A; B) = T r[(A − B)2]. We made cross-sections through every different one. n p 1 X q max BmkBml ≤ BjkBjl ; for all 1 ≤ k < l ≤ n; (3) m=1;:::;n 2 j=1 Unistochasticity solved for subsets Not being able to solve the unistochasticity problem in its full extent, we shall consider particular subsets of the Birkhoff polytope Bn of an arbitrary dimension n. Figure 1 presents a sketch of the set of bistochastic matrices visualizing the problems considered. Definition 3 Bistochastic matrix Wn, in which every element is the same and equal to 1=n, is called flat matrix. Definition 4 A one-dimensional set of bistochastic matrices obtained by a convex combination of the flat matrix Wn and any permutation matrix P is called a bistochastic ray, Figure 2: Cross-sections of the Birkhoff polytope B4 with permutation matrices in vertices and the flat matrix R = fR 2 B : R = αP + (1 − α)W ;P 2 P(n); α 2 [0; 1]g: (4) n α n α n W4 in the center. Dark blue represents unistochastic set U4, red represents the set C4 of matrices that satisfy the chain conditions, and the light blue denotes bistochastic matrices, that do not satisfy chain conditions. The Birkhoff polytope B4 possesses an interesting property – in every neighborhood of the flat unistochas- tic matrix W4 localized at its center there are non-unistochastic matrices. Hence there is a direction, in which deviation by arbitrary small leads to a matrix which is not unistochastic [2]. Figure 3: Cross-section of the Birkhoff polytope B4, determined by the directions V1 and V2 described in [2]. The flat matrix W4 in the center of the B4 is also localized at the boundary of the set U4. Colors have the same meaning as in Fig. 2 2 Figure 1: Sketch of the Birkhoff polytope Bn - a set of (n − 1) dimensions: the flat matrix Wn in the center, permutation matrices Pi at the corners. The corresponding rays R and counter-rays Re meet at Wn. The counter- ray Re ends at the counter-permutation matrix P~1 Bold lines denote complementary edges, while dashed lines Numerical estimation of volumes represent non-complementary edges. Region in gray – the triangle ∆P1;P2;Wn – represents sets proved to be unistochastic. Numerical estimation of the relative volume of the set of unistochastic matrices is shown in Fig. 4. Note that the relative volume of the set Cn of matrices satisfying the chain conditions approaches unity. Robust Hadamard matrices Definition 5 Set of robust Hadamard matrices for dimension n R Hii Hij H (n) = fH 2 Mn(C): 8i;j21;2;:::;n; i=6 j 2 H (2)g: Hji Hjj C The name refers to the fact that the Hadamard property is robust with respect to projections: HR remains R Hadamard after a projection Π2 onto a subspace spanned by two vectors of the basis used, Π2H Π2 2 H(2). These matrices are closely connected with following notions of skew and symmetric conference: Definition 6 A real Hadamard matrix H is a skew Hadamard matrix, written H 2 HS(n), if and only if: T H + H = 2I: Figure 4: Fraction fu of random bistochastic matrices, generated with respect to the flat measure, satisfying unis- tochasticity conditions (black circle – •) computed for dimensions n < 5. Empty symbols (white triangle – 4 R S R Lemma 7 Any robust Hadamard matrix H is sign-equivalent to a certain skew Hadamard matrix: H ≈ H . and white square – ) denote the fraction fc of bistochastic matrices, which fulfill all chain conditions. Dotted line is plotted to guide the eye. References [1] Y.H. Au-Yeng and Y.T. Poon, 3 × 3 Orthostochastic matrices and the convexity of generalized numerical ranges, Lin. Alg. Appl. 27, 69-79 (1979). [2] I. Bengtsson, A. Ericsson, M. Kus,´ W. Tadej, K. Zyczkowski,˙ Birkhoff’s polytope and unistochastic matrices, N = 3 and N = 4, Commun. Math. Phys. 259, 307-324 (2005). [3] C. Koukouvinos and S. Stylianou, On skew-Hadamard matrices, Discrete Mathematics 308, 2723-2731 (2008). [4] J.H. van Lint and J.J. Seidel, Equilateral point sets in elliptic geometry, Indag. Math. 28, 335-348 (1966)..
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