mlsanityData to 7 Oct 2026

Conditional Born machine for Monte Carlo event generation

arXiv 2205.07674 ↗First posted 16 May 2022quant-ph cs.LG hep-excited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 25 of its 70 references that are in the map. First posted on arXiv 16 May 2022.

Builds on

25 of 70 references.

References · 25

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13 more
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Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2023Trainability barriers and opportunities in quantum generative modelingcited by 35
  2. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 29
  3. 2024Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemblecited by 9
  4. 2025A Primer on Quantum Machine Learningcited by 9