Conditional Born machine for Monte Carlo events generation

• URL: http://arxiv.org/abs/2205.07674v1
• Date: Mon, 16 May 2022 13:41:03 GMT
• Title: Conditional Born machine for Monte Carlo events generation
• Authors: Oriel Kiss, Michele Grossi, Enrique Kajomovitz and Sofia Vallecorsa
• Abstract summary: This paper presents an application of Born machines to Monte Carlo simulations and extends their reach to conditional distributions. Born machines are used to generate muonic force carriers (MFC) events in high-energy-physics colliders experiments. Empirical evidences suggest that Born machines can reproduce the underlying distribution of datasets coming from Monte Carlo simulations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative modeling is a promising task for near-term quantum devices, which can use the stochastic nature of quantum measurements as 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 carriers (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 evidences suggest that Born machines can reproduce the underlying distribution of datasets coming from Monte Carlo simulations, and are competitive with classical machine learning-based generative models of similar complexity.

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