{
  "annotation_id": "1e7f6663-a15d-4b03-901e-3b9a1b30979b",
  "date_created": "2026-03-02T18:02:03.547000Z",
  "date_modified": "2026-03-02T18:02:03.547000Z",
  "file_hash": "501382c4c1b776a44d9ebb0cd4d295c86e7edd61af1e2a8f3dbd52e8a9ad1d6e",
  "private": false,
  "record": {
    "abstract": "Optimal Identification (OI) is a recently developed procedure for extracting\noptimal information about quantum Hamiltonians from experimental data using\nshaped control fields to drive the system in such a manner that dynamical\nmeasurements provide maximal information about its Hamiltonian. However, while\noptimal, OI is computationally expensive as initially presented. Here, we\ndescribe the unification of OI with highly efficient global, nonlinear\nmap-facilitated data inversion procedures. This combination is expected to make\nOI techniques more suitable for laboratory implementation. A simulation of\nmap-facilitated OI is performed demonstrating that the input-output maps can\ngreatly accelerate the inversion process.",
    "arxiv_id": "quant-ph/0312212",
    "authors": [
      "JM Geremia",
      "Herschel A. Rabitz"
    ],
    "categories": [
      "quant-ph"
    ],
    "doi": "10.1103/PhysRevA.70.023804",
    "title": "Efficient extraction of quantum Hamiltonians from optimal laboratory   data",
    "url": "https://arxiv.org/abs/quant-ph/0312212"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "03cbe7fd-9bf6-471b-93fe-e277c5bff62e",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}