4764c109eca254d24d3b · QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments

{
  "title_en": "QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2018-10-17",
  "date_original": "October 17, 2018",
  "url": "https://www.nist.gov/publications/qflow-lite-dataset-machine-learning-approach-charge-states-quantum-dot-experiments",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Justyna P. Zwolak , Sandesh Kalantre, Xingyao Wu , Stephen Ragole, Jacob M. Taylor",
  "abstract_excerpt_en": "",
  "id": "4764c109eca254d24d3b",
  "sources": [
    "NIST Publications"
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  "source_records": [
    {
      "title_en": "QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments",
      "designation": "",
      "series": "",
      "status": "",
      "date": "2018-10-17",
      "date_original": "October 17, 2018",
      "url": "https://www.nist.gov/publications/qflow-lite-dataset-machine-learning-approach-charge-states-quantum-dot-experiments",
      "source": "NIST Publications",
      "display_en": "",
      "authors_en": "Justyna P. Zwolak , Sandesh Kalantre, Xingyao Wu , Stephen Ragole, Jacob M. Taylor",
      "abstract_excerpt_en": ""
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