  A reliable determination of probability density functions from data samples
is still a relevant topic in scientific applications. In this work we
investigate the possibility of defining an algorithm for density function
estimation using adiabatic quantum computing. Starting from a sample of a
one-dimensional distribution, we define a classical-to-quantum data embedding
procedure which maps the empirical cumulative distribution function of the
sample into time dependent Hamiltonian using adiabatic quantum evolution. The
obtained Hamiltonian is then projected into a quantum circuit using the time
evolution operator. Finally, the probability density function of the sample is
obtained using quantum hardware differentiation through the parameter shift
rule algorithm. We present successful numerical results for predefined known
distributions and high-energy physics Monte Carlo simulation samples.
