368,854 research outputs found
Speed-up via Quantum Sampling
The Markov Chain Monte Carlo method is at the heart of efficient
approximation schemes for a wide range of problems in combinatorial enumeration
and statistical physics. It is therefore very natural and important to
determine whether quantum computers can speed-up classical mixing processes
based on Markov chains. To this end, we present a new quantum algorithm, making
it possible to prepare a quantum sample, i.e., a coherent version of the
stationary distribution of a reversible Markov chain. Our algorithm has a
significantly better running time than that of a previous algorithm based on
adiabatic state generation. We also show that our methods provide a speed-up
over a recently proposed method for obtaining ground states of (classical)
Hamiltonians.Comment: 8 pages, fixed some minor typo
Theory of the Quantum Speed Up
Insofar as quantum computation is faster than classical, it appears to be
irreversible. In all quantum algorithms found so far the speed-up depends on
the extra-dynamical irreversible projection representing quantum measurement.
Quantum measurement performs a computation that dynamical computation cannot
accomplish as efficiently.Comment: 10 pages, RevTex, 1 page of 3 figure
Augmented reality usage for prototyping speed up
The first part of the article describes our approach for solution of this
problem by means of Augmented Reality. The merging of the real world model and
digital objects allows streamline the work with the model and speed up the
whole production phase significantly. The main advantage of augmented reality
is the possibility of direct manipulation with the scene using a portable
digital camera. Also adding digital objects into the scene could be done using
identification markers placed on the surface of the model. Therefore it is not
necessary to work with special input devices and lose the contact with the real
world model. Adjustments are done directly on the model. The key problem of
outlined solution is the ability of identification of an object within the
camera picture and its replacement with the digital object. The second part of
the article is focused especially on the identification of exact position and
orientation of the marker within the picture. The identification marker is
generalized into the triple of points which represents a general plane in
space. There is discussed the space identification of these points and the
description of representation of their position and orientation be means of
transformation matrix. This matrix is used for rendering of the graphical
objects (e. g. in OpenGL and Direct3D).Comment: Keywords: augmented reality, prototyping, pose estimation,
transformation matri
Proposals which speed-up function-space MCMC
Inverse problems lend themselves naturally to a Bayesian formulation, in
which the quantity of interest is a posterior distribution of state and/or
parameters given some uncertain observations. For the common case in which the
forward operator is smoothing, then the inverse problem is ill-posed.
Well-posedness is imposed via regularisation in the form of a prior, which is
often Gaussian. Under quite general conditions, it can be shown that the
posterior is absolutely continuous with respect to the prior and it may be
well-defined on function space in terms of its density with respect to the
prior. In this case, by constructing a proposal for which the prior is
invariant, one can define Metropolis-Hastings schemes for MCMC which are
well-defined on function space, and hence do not degenerate as the dimension of
the underlying quantity of interest increases to infinity, e.g. under mesh
refinement when approximating PDE in finite dimensions. However, in practice,
despite the attractive theoretical properties of the currently available
schemes, they may still suffer from long correlation times, particularly if the
data is very informative about some of the unknown parameters. In fact, in this
case it may be the directions of the posterior which coincide with the (already
known) prior which decorrelate the slowest. The information incorporated into
the posterior through the data is often contained within some
finite-dimensional subspace, in an appropriate basis, perhaps even one defined
by eigenfunctions of the prior. We aim to exploit this fact and improve the
mixing time of function-space MCMC by careful rescaling of the proposal. To
this end, we introduce two new basic methods of increasing complexity,
involving (i) characteristic function truncation of high frequencies and (ii)
hessian information to interpolate between low and high frequencies
Job Combinations and Speed-up in Steel
[Excerpt] The Rail Mill Manning Agreement at South works in not unique. Reducing labor costs by combining jobs is a key part of the steel companies\u27 strategy for regaining profitability. MCLR has conducted a survey of five other mills to find out what the companies are doing to reduce the work force, and speed up work. We print here a summary of our preliminary findings
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