Generating pseudo-random discrete probability distributions

Abstract

The generation of pseudo-random discrete probability distributions is of paramount importance for a wide range of stochastic simulations spanning from Monte Carlo methods to the random sampling of quantum states for investigations in quantum information science. In spite of its significance, a thorough exposition of such a procedure is lacking in the literature. In this article we present relevant details concerning the numerical implementation and applicability of what we call the iid, normalization, and trigonometric methods for generating an unbiased probability vector $\mathbf{p}=(p_1,\cdots ,p_d)$. An immediate application of these results regarding the generation of pseudo-random pure quantum states is also described.

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