283,508 research outputs found
Quantum Computing and Nuclear Magnetic Resonance
Quantum information processing is the use of inherently quantum mechanical
phenomena to perform information processing tasks that cannot be achieved using
conventional classical information technologies. One famous example is quantum
computing, which would permit calculations to be performed that are beyond the
reach of any conceivable conventional computer. Initially it appeared that
actually building a quantum computer would be extremely difficult, but in the
last few years there has been an explosion of interest in the use of techniques
adapted from conventional liquid state nuclear magnetic resonance (NMR)
experiments to build small quantum computers. After a brief introduction to
quantum computing I will review the current state of the art, describe some of
the topics of current interest, and assess the long term contribution of NMR
studies to the eventual implementation of practical quantum computers capable
of solving real computational problems.Comment: 8 pages pdf including 6 figures. Perspectives article commissioned by
PhysChemCom
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Solving large scale linear programming
The interior point method (IPM) is now well established as a competitive technique for solving very large scale linear programming problems. The leading variant of the interior point method is the primal dual - predictor corrector algorithm due to Mehrotra. The main computational steps of this algorithm are the repeated calculation and solution of a large sparse positive definite system of equations.
We describe an implementation of the predictor corrector IPM algorithm on MasPar, a massively parallel SIMD computer. At the heart of the implemen-tation is a parallel Cholesky factorization algorithm for sparse matrices. Our implementation uses a new scheme of mapping the matrix onto the processor grid of the MasPar, that results in a more efficient Cholesky factorization than previously suggested schemes.
The IPM implementation uses the parallel unit of MasPar to speed up the factorization and other computationally intensive parts of the IPM. An impor-tant part of this implementation is the judicious division of data and computation between the front-end computer, that runs the main IPM algorithm, and the par-allel unit. Performanc
Parallel computing for the finite element method
A finite element method is presented to compute time harmonic microwave
fields in three dimensional configurations. Nodal-based finite elements have
been coupled with an absorbing boundary condition to solve open boundary
problems. This paper describes how the modeling of large devices has been made
possible using parallel computation, New algorithms are then proposed to
implement this formulation on a cluster of workstations (10 DEC ALPHA 300X) and
on a CRAY C98. Analysis of the computation efficiency is performed using simple
problems. The electromagnetic scattering of a plane wave by a perfect electric
conducting airplane is finally given as example
NMR Quantum Computation
In this article I will describe how NMR techniques may be used to build
simple quantum information processing devices, such as small quantum computers,
and show how these techniques are related to more conventional NMR experiments.Comment: Pedagogical mini review of NMR QC aimed at NMR folk. Commissioned by
Progress in NMR Spectroscopy (in press). 30 pages RevTex including 15 figures
(4 low quality postscript images
Algorithmic Cooling of Spins: A Practicable Method for Increasing Polarization
An efficient technique to generate ensembles of spins that are highly
polarized by external magnetic fields is the Holy Grail in Nuclear Magnetic
Resonance (NMR) spectroscopy. Since spin-half nuclei have steady-state
polarization biases that increase inversely with temperature, spins exhibiting
high polarization biases are considered cool, even when their environment is
warm. Existing spin-cooling techniques are highly limited in their efficiency
and usefulness. Algorithmic cooling is a promising new spin-cooling approach
that employs data compression methods in open systems. It reduces the entropy
of spins on long molecules to a point far beyond Shannon's bound on reversible
entropy manipulations (an information-theoretic version of the 2nd Law of
Thermodynamics), thus increasing their polarization. Here we present an
efficient and experimentally feasible algorithmic cooling technique that cools
spins to very low temperatures even on short molecules. This practicable
algorithmic cooling could lead to breakthroughs in high-sensitivity NMR
spectroscopy in the near future, and to the development of scalable NMR quantum
computers in the far future. Moreover, while the cooling algorithm itself is
classical, it uses quantum gates in its implementation, thus representing the
first short-term application of quantum computing devices.Comment: 24 pages (with annexes), 3 figures (PS). This version contains no
major content changes: fixed bibliography & figures, modified
acknowledgement
Simulation modelling and visualisation: toolkits for building artificial worlds
Simulations users at all levels make heavy use of compute resources to drive computational
simulations for greatly varying applications areas of research using different simulation
paradigms. Simulations are implemented in many software forms, ranging from highly standardised
and general models that run in proprietary software packages to ad hoc hand-crafted
simulations codes for very specific applications. Visualisation of the workings or results of a
simulation is another highly valuable capability for simulation developers and practitioners.
There are many different software libraries and methods available for creating a visualisation
layer for simulations, and it is often a difficult and time-consuming process to assemble a
toolkit of these libraries and other resources that best suits a particular simulation model. We
present here a break-down of the main simulation paradigms, and discuss differing toolkits and
approaches that different researchers have taken to tackle coupled simulation and visualisation
in each paradigm
Validation of Ultrahigh Dependability for Software-Based Systems
Modern society depends on computers for a number of critical tasks in which failure can have very high costs. As a consequence, high levels of dependability (reliability, safety, etc.) are required from such computers, including their software. Whenever a quantitative approach to risk is adopted, these requirements must be stated in quantitative terms, and a rigorous demonstration of their being attained is necessary. For software used in the most critical roles, such demonstrations are not usually supplied. The fact is that the dependability requirements often lie near the limit of the current state of the art, or beyond, in terms not only of the ability to satisfy them, but also, and more often, of the ability to demonstrate that they are satisfied in the individual operational products (validation). We discuss reasons why such demonstrations cannot usually be provided with the means available: reliability growth models, testing with stable reliability, structural dependability modelling, as well as more informal arguments based on good engineering practice. We state some rigorous arguments about the limits of what can be validated with each of such means. Combining evidence from these different sources would seem to raise the levels that can be validated; yet this improvement is not such as to solve the problem. It appears that engineering practice must take into account the fact that no solution exists, at present, for the validation of ultra-high dependability in systems relying on complex software
What is a quantum computer, and how do we build one?
The DiVincenzo criteria for implementing a quantum computer have been seminal
in focussing both experimental and theoretical research in quantum information
processing. These criteria were formulated specifically for the circuit model
of quantum computing. However, several new models for quantum computing
(paradigms) have been proposed that do not seem to fit the criteria well. The
question is therefore what are the general criteria for implementing quantum
computers. To this end, a formal operational definition of a quantum computer
is introduced. It is then shown that according to this definition a device is a
quantum computer if it obeys the following four criteria: Any quantum computer
must (1) have a quantum memory; (2) facilitate a controlled quantum evolution
of the quantum memory; (3) include a method for cooling the quantum memory; and
(4) provide a readout mechanism for subsets of the quantum memory. The criteria
are met when the device is scalable and operates fault-tolerantly. We discuss
various existing quantum computing paradigms, and how they fit within this
framework. Finally, we lay out a roadmap for selecting an avenue towards
building a quantum computer. This is summarized in a decision tree intended to
help experimentalists determine the most natural paradigm given a particular
physical implementation
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