{
  "annotation_id": "8a0dd459-a068-41c6-80b4-d3710cd7cf74",
  "date_created": "2026-03-02T18:02:03.314000Z",
  "date_modified": "2026-03-02T18:02:03.314000Z",
  "file_hash": "d09c6cdcdf5dcbae3c5b2d91cea8f71c63474b4e157233981c014a6225996c32",
  "private": false,
  "record": {
    "abstract": "Genetic learning algorithms are widely used to control ultrafast optical\npulse shapes for photo-induced quantum control of atoms and molecules. An\nunresolved issue is how to use the solutions found by these algorithms to learn\nabout the system\u0027s quantum dynamics. We propose a simple method based on\ncovariance analysis of the control space, which can reveal the degrees of\nfreedom in the effective control Hamiltonian. We have applied this technique to\nstimulated Raman scattering in liquid methanol. A simple model of two-mode\nstimulated Raman scattering is consistent with the results.",
    "arxiv_id": "quant-ph/0401018",
    "authors": [
      "J. L. White",
      "B. J. Pearson",
      "P. H. Bucksbaum"
    ],
    "categories": [
      "quant-ph"
    ],
    "doi": "10.1088/0953-4075/37/24/L02",
    "title": "Extracting quantum dynamics from genetic learning algorithms through   principal control analysis",
    "url": "https://arxiv.org/abs/quant-ph/0401018"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "e0df87b6-7f6f-40ba-923b-5b2c7ca007cd",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}