477 research outputs found
A limit for large -charge correlators in theories
Using supersymmetric localization, we study the sector of chiral primary
operators with large -charge in
four-dimensional superconformal theories in the weak coupling
regime , where is kept fixed as
, representing the gauge theory coupling(s). In this limit,
correlation functions of these operators behave in a simple way, with
an asymptotic behavior of the form , modulo
corrections, with for a gauge
algebra and a universal function . As a
by-product we find several new formulas both for the partition function as well
as for perturbative correlators in gauge theory
with fundamental hypermultiplets
Integrated Water Resources Management Curriculum in the United States: Results of a Recent Survey
A generalization of the Heine--Stieltjes theorem
We extend the Heine-Stieltjes Theorem to concern all (non-degenerate)
differential operators preserving the property of having only real zeros. This
solves a conjecture of B. Shapiro. The new methods developed are used to
describe intricate interlacing relations between the zeros of different pairs
of solutions. This extends recent results of Bourget, McMillen and Vargas for
the Heun equation and answers their question on how to generalize their results
to higher degrees. Many of the results are new even for the classical case.Comment: 12 pages, typos corrected and refined the interlacing theorem
Localization for Random Unitary Operators
We consider unitary analogs of dimensional Anderson models on
defined by the product where is a deterministic
unitary and is a diagonal matrix of i.i.d. random phases. The
operator is an absolutely continuous band matrix which depends on a
parameter controlling the size of its off-diagonal elements. We prove that the
spectrum of is pure point almost surely for all values of the
parameter of . We provide similar results for unitary operators defined on
together with an application to orthogonal polynomials on the unit
circle. We get almost sure localization for polynomials characterized by
Verblunski coefficients of constant modulus and correlated random phases
Fractional Moment Estimates for Random Unitary Operators
We consider unitary analogs of dimensional Anderson models on
defined by the product where is a deterministic
unitary and is a diagonal matrix of i.i.d. random phases. The
operator is an absolutely continuous band matrix which depends on
parameters controlling the size of its off-diagonal elements. We adapt the
method of Aizenman-Molchanov to get exponential estimates on fractional moments
of the matrix elements of , provided the
distribution of phases is absolutely continuous and the parameters correspond
to small off-diagonal elements of . Such estimates imply almost sure
localization for
On the energy growth of some periodically driven quantum systems with shrinking gaps in the spectrum
We consider quantum Hamiltonians of the form H(t)=H+V(t) where the spectrum
of H is semibounded and discrete, and the eigenvalues behave as E_n~n^\alpha,
with 0<\alpha<1. In particular, the gaps between successive eigenvalues decay
as n^{\alpha-1}. V(t) is supposed to be periodic, bounded, continuously
differentiable in the strong sense and such that the matrix entries with
respect to the spectral decomposition of H obey the estimate
|V(t)_{m,n}|0,
p>=1 and \gamma=(1-\alpha)/2. We show that the energy diffusion exponent can be
arbitrarily small provided p is sufficiently large and \epsilon is small
enough. More precisely, for any initial condition \Psi\in Dom(H^{1/2}), the
diffusion of energy is bounded from above as _\Psi(t)=O(t^\sigma) where
\sigma=\alpha/(2\ceil{p-1}\gamma-1/2). As an application we consider the
Hamiltonian H(t)=|p|^\alpha+\epsilon*v(\theta,t) on L^2(S^1,d\theta) which was
discussed earlier in the literature by Howland
Eigenvalue distributions from a star product approach
We use the well-known isomorphism between operator algebras and function
spaces equipped with a star product to study the asymptotic properties of
certain matrix sequences in which the matrix dimension tends to infinity.
Our approach is based on the coherent states which allow for a
systematic 1/D expansion of the star product. This produces a trace formula for
functions of the matrix sequence elements in the large- limit which includes
higher order (finite-) corrections. From this a variety of analytic results
pertaining to the asymptotic properties of the density of states, eigenstates
and expectation values associated with the matrix sequence follows. It is shown
how new and existing results in the settings of collective spin systems and
orthogonal polynomial sequences can be readily obtained as special cases. In
particular, this approach allows for the calculation of higher order
corrections to the zero distributions of a large class of orthogonal
polynomials.Comment: 25 pages, 8 figure
Can biological quantum networks solve NP-hard problems?
There is a widespread view that the human brain is so complex that it cannot
be efficiently simulated by universal Turing machines. During the last decades
the question has therefore been raised whether we need to consider quantum
effects to explain the imagined cognitive power of a conscious mind.
This paper presents a personal view of several fields of philosophy and
computational neurobiology in an attempt to suggest a realistic picture of how
the brain might work as a basis for perception, consciousness and cognition.
The purpose is to be able to identify and evaluate instances where quantum
effects might play a significant role in cognitive processes.
Not surprisingly, the conclusion is that quantum-enhanced cognition and
intelligence are very unlikely to be found in biological brains. Quantum
effects may certainly influence the functionality of various components and
signalling pathways at the molecular level in the brain network, like ion
ports, synapses, sensors, and enzymes. This might evidently influence the
functionality of some nodes and perhaps even the overall intelligence of the
brain network, but hardly give it any dramatically enhanced functionality. So,
the conclusion is that biological quantum networks can only approximately solve
small instances of NP-hard problems.
On the other hand, artificial intelligence and machine learning implemented
in complex dynamical systems based on genuine quantum networks can certainly be
expected to show enhanced performance and quantum advantage compared with
classical networks. Nevertheless, even quantum networks can only be expected to
efficiently solve NP-hard problems approximately. In the end it is a question
of precision - Nature is approximate.Comment: 38 page
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