Conditional Born machine for Monte Carlo event generation

TL;DR

This paper explores the use of quantum Born machines for generating complex probability distributions in Monte Carlo simulations, demonstrating their potential in high-energy physics and quantum computing applications.

Contribution

It introduces the application of Born machines to multivariate and conditional distributions, extending their use in Monte Carlo event generation on quantum hardware.

Findings

Born machines can reproduce marginal distributions from Monte Carlo data.

They successfully generate correlations in high-energy physics simulations.

Models perform on noisy simulators and real quantum hardware.

Abstract

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.