{
  "annotation_id": "046ad24c-eba4-4fea-917a-af4475563448",
  "date_created": "2026-02-17T05:53:20.400000Z",
  "date_modified": "2026-02-17T05:53:20.400000Z",
  "file_hash": "92699521d5ad9fd16d413ffd7e51b45846c96f13ab917d60059032ffacf31bfb",
  "private": false,
  "record": {
    "abstract": "We develop a data-driven framework for identifying non-Markovian dynamical equations of motion for open quantum systems. Starting from the Nakajima--Zwanzig formalism, we vectorize the reduced density matrix into a four-dimensional state vector and cast the dynamics as a Volterra integro-differential equation with an operator-valued memory kernel. The learning task is then formulated as a constrained optimization problem over the admissible operator space, where correlation functions are approximated by rational functions using Pad\\\u0027e approximants. We establish well-posedness of the learnin",
    "arxiv_id": "2601.09075",
    "authors": [
      "Jimmie Adriazola",
      "Katarzyna Roszak"
    ],
    "categories": [
      "quant-ph",
      "math.OC"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Learning Volterra Kernels for Non-Markovian Open Quantum Systems",
    "url": "https://arxiv.org/abs/2601.09075",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "98fad9df-ce71-4efa-a9cc-6d68e99a9498",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}