2,276 research outputs found
Optimal Summation and Integration by Deterministic, Randomized, and Quantum Algorithms
We survey old and new results about optimal algorithms for summation of
finite sequences and for integration of functions from Hoelder or Sobolev
spaces. First we discuss optimal deterministic and randomized algorithms. Then
we add a new aspect, which has not been covered before on conferences about
(quasi-) Monte Carlo methods: quantum computation. We give a short introduction
into this setting and present recent results of the authors on optimal quantum
algorithms for summation and integration. We discuss comparisons between the
three settings. The most interesting case for Monte Carlo and quantum
integration is that of moderate smoothness k and large dimension d which, in
fact, occurs in a number of important applied problems. In that case the
deterministic exponent is negligible, so the n^{-1/2} Monte Carlo and the
n^{-1} quantum speedup essentially constitute the entire convergence rate. We
observe that -- there is an exponential speed-up of quantum algorithms over
deterministic (classical) algorithms, if k/d tends to zero; -- there is a
(roughly) quadratic speed-up of quantum algorithms over randomized classical
algorithms, if k/d is small.Comment: 13 pages, contribution to the 4th International Conference on Monte
Carlo and Quasi-Monte Carlo Methods, Hong Kong 200
Some Results on the Complexity of Numerical Integration
This is a survey (21 pages, 124 references) written for the MCQMC 2014
conference in Leuven, April 2014. We start with the seminal paper of Bakhvalov
(1959) and end with new results on the curse of dimension and on the complexity
of oscillatory integrals. Some small errors of earlier versions are corrected
Quantum Approximation II. Sobolev Embeddings
A basic problem of approximation theory, the approximation of functions from
the Sobolev space W_p^r([0,1]^d) in the norm of L_q([0,1]^d), is considered
from the point of view of quantum computation. We determine the quantum query
complexity of this problem (up to logarithmic factors). It turns out that in
certain regions of the domain of parameters p,q,r,d quantum computation can
reach a speedup of roughly squaring the rate of convergence of classical
deterministic or randomized approximation methods. There are other regions were
the best possible rates coincide for all three settings.Comment: 23 pages, paper submitted to the Journal of Complexit
Quantum Integration in Sobolev Classes
We study high dimensional integration in the quantum model of computation. We
develop quantum algorithms for integration of functions from Sobolev classes
and analyze their convergence rates. We also prove lower
bounds which show that the proposed algorithms are, in many cases, optimal
within the setting of quantum computing. This extends recent results of Novak
on integration of functions from H\"older classes.Comment: Paper submitted to the Journal of Complexity. 28 page
Tensor Product Approximation (DMRG) and Coupled Cluster method in Quantum Chemistry
We present the Copupled Cluster (CC) method and the Density matrix
Renormalization Grooup (DMRG) method in a unified way, from the perspective of
recent developments in tensor product approximation. We present an introduction
into recently developed hierarchical tensor representations, in particular
tensor trains which are matrix product states in physics language. The discrete
equations of full CI approximation applied to the electronic Schr\"odinger
equation is casted into a tensorial framework in form of the second
quantization. A further approximation is performed afterwards by tensor
approximation within a hierarchical format or equivalently a tree tensor
network. We establish the (differential) geometry of low rank hierarchical
tensors and apply the Driac Frenkel principle to reduce the original
high-dimensional problem to low dimensions. The DMRG algorithm is established
as an optimization method in this format with alternating directional search.
We briefly introduce the CC method and refer to our theoretical results. We
compare this approach in the present discrete formulation with the CC method
and its underlying exponential parametrization.Comment: 15 pages, 3 figure
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