{
  "annotation_id": "11b34552-68ad-44bf-9f7f-fd2d6b82fec2",
  "date_created": "2026-02-17T05:53:23.753000Z",
  "date_modified": "2026-02-17T05:53:23.753000Z",
  "file_hash": "80a5d3b3e92231e16176b88ec5c8fd32e5d7cd796a4c01b6ae298e2864d74531",
  "private": false,
  "record": {
    "abstract": "Quantum annealing is typically regarded as a tool for combinatorial optimization, but its coherent dynamics also offer potential for machine learning. We present a model that encodes classical data into an Ising Hamiltonian, evolves it on a quantum annealer, and uses the resulting probability distributions as feature maps for classification. Experiments on the quantum annealer machine with the Digits dataset, together with simulations on MNIST, demonstrate that short annealing times yield higher classification accuracy, while longer times reduce accuracy but lower sampling costs. We introduce the participation ratio as a measure of the effective model size and show its strong correlation with generalization.",
    "arxiv_id": "2601.09938",
    "authors": [
      "Akitada Sakurai",
      "Aoi Hayashi",
      "Tadayoshi Matumori",
      "Daisuke Kaji",
      "Tadashi Kadowaki",
      "Kae Nemoto"
    ],
    "categories": [
      "quant-ph"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Beyond Optimization: Harnessing Quantum Annealer Dynamics for Machine Learning",
    "url": "https://arxiv.org/abs/2601.09938",
    "version": "v2"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "87eb072c-dddf-4a19-a8ed-a1e18861bac4",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}