  We deal with the problem, initiated in [8], of finding randomized and quantum
complexity of initial-value problems. We showed in [8] that a speed-up in both
settings over the worst-case deterministic complexity is possible. In the
present paper we prove, by defining new algorithms, that further improvement in
upper bounds on the randomized and quantum complexity can be achieved. In the
H\"older class of right-hand side functions with r continuous bounded partial
derivatives, with r-th derivative being a H\"older function with exponent \rho,
the \epsilon-complexity is shown to be O((1/\epsilon)^{1/(r+\rho+1/3)}) in the
randomized setting, and O((1/\epsilon)^{1/(r+\rho+1/2)}) on a quantum computer
(up to logarithmic factors). This is an improvement for the general problem
over the results from [8]. The gap still remaining between upper and lower
bounds on the complexity is further discussed for a special problem. We
consider scalar autonomous problems, with the aim of computing the solution at
the end point of the interval of integration. For this problem, we fill up the
gap by establishing (essentially) matching upper and lower complexity bounds.
We show that the complexity in this case is of order
(1/\epsilon)^{1/(r+\rho+1/2)} in the randomized setting, and
(1/\epsilon)^{1/(r+\rho+1)} in the quantum setting (again up to logarithmic
factors).
