  We use a variational method for generating probability distributions,
specifically, the Uniform, the Normal, the Binomial distribution, and the
Poisson distribution. To do the same, we use many different architectures for
the two, three and four-qubit cases using the Jensen-Shannon divergence as our
objective function. We use gradient descent with momentum as our optimization
scheme instead of conventionally used gradient descent. To calculate the
gradient, we use the parameter shift rule, whose formulation we modify to take
the probability values as outputs instead of the conventionally taken
expectation values. We see that this method can approximate probability
distributions, and there exists a specific architecture which outperforms other
architectures, and this architecture depends on the number of qubits. The four,
three and two-qubit cases consist of a parameterized layer followed by an
entangling layer; a parameterized layer followed by an entangling layer, which
is followed by a parameterized layer and only parameterized layers,
respectively.
