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Monte Carlo simulation of the atomic master equation for spontaneous emission

Dum, R., Zoller, P., Ritsch, H.·1992·Physical Review A

Abstract

A Monte Carlo simulation of the atomic master equation for spontaneous emission in terms of atomic wave functions is developed. Realizations of the time evolution of atomic wave functions are constructed that correspond to an ensemble of atoms driven by laser light undergoing a sequence of spontaneous emissions. The atomic decay times are drawn according to the photon count distribution of the driven atom. Each quantum jump of the atomic electron projects the atomic wave function to the ground state of the atom. Our theory is based on a stochastic interpretation and generalization of Mollow's pure-state analysis of resonant light scattering, and the Srinivas-Davies theory of continuous measurements in photodetection. An extension of the theory to include mechanical light effects and a generalization to atomic systems with Zeeman substructure are given. We illustrate the method by simulating the solutions of the optical Bloch equations for two-level systems, and laser cooling of a two-level atom in an ion trap where the center-of-mass motion of the atom is described quantum mechanically.

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Metadata

Authors
Dum, R., Zoller, P., & Ritsch, H.
Venue
Physical Review A
Published
1992-04-01
Volume
45
Issue
7
Pages
4879-4887
ISSN
  • 0556-2791 (ISSN-L)
  • 1050-2947
  • 1094-1622
OpenAlex
W2058324353
Cited by
652
References
0
FWCI
17.25
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Monte Carlo simulation of the atomic master equation for spontaneous emission. https://app.cochat.ai/papers/10.1103/physreva.45.4879
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Dum, R., Zoller, P., & Ritsch, H. (1992). Monte Carlo simulation of the atomic master equation for spontaneous emission. Physical Review A. https://doi.org/10.1103/physreva.45.4879

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.