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Asked on June 12, 2018 in Sampling/Monte Carlo.
Hello Jaffar,
I’m quite familiar with Montecarlo methods applied to stochastic systemic models, used them for a couple of decades now, and ran my PhD thesis on them. However there are certain high dimensionality problems (related to certain multivariate analysis problems) where a curse of dimensionality solution space together with sparse distribution yield the number of trials very high to achieve a small error. Even if I know that I can pump billion of trials to get a result and I can run them in a reasonable time in some power computer I’m still curious on how it compares with using QC for that, and what it takes to do so. Obviously the problem I posted is a “toy problem” useful to understand the very basics involved. So far I’ve found little or no literature on the subject and this is the reason of my posting. You can rearrange my question as “how to implement a random variable of a given distribution using QC”,. Thanks, Pedro.
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Asked on June 1, 2018 in Sampling/Monte Carlo.
Looks like one good lead to follow, I’ll check that information.
Best Regards, Pedro.
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