Conditional Born machine for Monte Carlo event generation
This work addresses the challenge of simulating beyond-the-standard-model particle interactions for dark matter research, representing an incremental advancement in quantum generative modeling for specific physics applications.
The paper tackled the problem of generating multivariate and conditional distributions for Monte Carlo event generation in high-energy physics, specifically for muonic force carrier events, by applying Born machines on quantum hardware and simulators, with empirical evidence showing they can reproduce marginal distributions and correlations from simulation data.
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.