  Generative modeling is a promising task for near-term quantum devices, which
can use the stochastic nature of quantum measurements as a random source. So
called Born machines are purely quantum models and promise to generate
probability distributions in a quantum way, inaccessible to classical
computers. This paper presents an application of Born machines to Monte Carlo
simulations and extends their reach to multivariate and conditional
distributions. Models are run on (noisy) simulators and IBM Quantum
superconducting quantum hardware.
  More specifically, Born machines are used to generate muonic force carrier
(MFC) events resulting from scattering processes between muons and the detector
material in high-energy physics colliders experiments. MFCs are bosons
appearing in beyond-the-standard-model theoretical frameworks, which are
candidates for dark matter. Empirical evidence suggests that Born machines can
reproduce the marginal distributions and correlations of data sets from Monte
Carlo simulations.
