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On the Power of Quantum Algorithms for Vector Valued Mean Computation
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Stefan Heinrich
Published/Copyright:
May 9, 2008
We study computation of the mean of sequences with values in finite dimensional normed spaces and compare the computational power of classical randomized with that of quantum algorithms for this problem. It turns out that in contrast to the known superiority of quantum algorithms in the scalar case, in high dimensional LMP spaces classical randomized algorithms are essentially as powerful as quantum algorithms.
Published Online: 2008-05-09
Published in Print: 2004-12
© de Gruyter 2004
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Articles in the same Issue
- Foreword
- An outline of quasi-probability*: Why quasi-Monte-Carlo methods are statistically valid and how their errors can be estimated statistically
- QMC techniques for CAT bond pricing*
- Parallel Quasi-Monte Carlo Methods for Linear Algebra Problems
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- Trajectory Splitting by Restricted Replication
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- Stochastic Eulerian model for the flow simulation in porous media. Unconfined aquifers*
- Solution of the Space-dependent Wigner Equation Using a Particle Model
- Subgrid Modeling of Filtration in a Porous Medium with Multiscale Log-Stable permeability
- Comparison of Quasi-Monte Carlo-Based Methods for Simulation of Markov Chains
- Monte Carlo methods for fissured porous media: a gridless approach*
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- Adaptive adjoint Monte Carlo simulation for the uncertainty
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- System availability and reliability analysis by direct Monte Carlo with biasing
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