  This paper provides a framework in which multilevel Monte Carlo and
continuous level Monte Carlo can be compared. In continuous level Monte Carlo
the level of refinement is determined by an exponentially distributed random
variable, which therefore heavily influences the computational complexity. We
propose in this paper a variant of the algorithm, where the exponentially
distributed random variable is generated by a quasi Monte Carlo sequence,
resulting in a significant variance reduction. In the examples presented the
quasi continuous level Monte Carlo algorithm outperforms multilevel and
continuous level Monte Carlo by a clear margin.
