arXiv subjects

Markus Hunziker

Publications and source records attributed to Markus Hunziker.

21 records · Page 2Linked to original sources

arXiv2006

Kostant modules in blocks of category ${\mathcal O}_S$

In this paper the authors investigate infinite-dimensional representations $L$ in blocks of the relative (parabolic) category ${\mathcal O}_S$ for a complex simple Lie algebra, having the property that the cohomology of the nilradical with coefficients in $L$ ``looks like'' the cohomology with coefficients in a finite-dimensional module, as in Kostant's theorem. A complete classification of these ``Kostant modules'' in regular blocks for maximal parabolics in the simply laced types is given. A complete classification is also given in arbitrary (singular) blocks for Hermitian symmetric categories.

math.RT↗

arXiv2006

Multiplication of polynomials on Hermitian symmetric spaces and Littlewood-Richardson coefficients

Let K be a complex reductive algebraic group and V a representation of K. Let S denote the ring of polynomials on V. Assume that the action of K on S is multiplicity free. If V_λ is an irreducible representation of K, let S_λ denote the corresponding isotypic component of S. Write S_λ S_μ for the subspace of S spanned by products of S_λ and S_μ. If V_ν occurs as an irreducible constituent of the tensor product of V_λ and V_μ, is it true that S_ν is contained in S_λ S_μ? We investigate this question for representations arising in the context of Hermitian symmetric pairs. We show that the answer is yes in some cases and, using an earlier result of Ruitenburg, that in the remaining classical cases, the answer is yes provided that a conjecture of Stanley on the multiplication of Jack polynomials is true. We also show how the conjecture connects multiplication in the ring S to the usual Littlewood-Richardson rule.

math.RT↗

arXiv2003

The geometry of quantum learning

Concept learning provides a natural framework in which to place the problems solved by the quantum algorithms of Bernstein-Vazirani and Grover. By combining the tools used in these algorithms--quantum fast transforms and amplitude amplification--with a novel (in this context) tool--a solution method for geometrical optimization problems--we derive a general technique for quantum concept learning. We name this technique "Amplified Impatient Learning" and apply it to construct quantum algorithms solving two new problems: BATTLESHIP and MAJORITY, more efficiently than is possible classically.

quant-ph↗