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
