{
  "annotation_id": "88375f54-3467-42f5-a035-d09054fb3a68",
  "date_created": "2026-03-02T18:01:45.270000Z",
  "date_modified": "2026-03-02T18:01:45.270000Z",
  "file_hash": "ee89dee4f6fafdf17f161ee4deb43949e8258800bc74d218f91e1bde1afab1c6",
  "private": false,
  "record": {
    "abstract": "We describe a model element able to perform universal stochastic\napproximations of continuous multivariable functions in both neuron-like and\nquantum form. The implementation of this model in the form of a multi-barrier,\nmultiple-slit system is proposed and it is demonstrated that this single\nneuron-like model is able to perform the XOR function unrealizable with single\nclassical neuron. For the simplified waveguide variant of this model it is\nproved for different interfering quantum alternatives with no correlated\nadjustable parameters, that the system can approximate any continuous function\nof many variables. This theorem is applied to the 2-input quantum neural model\nbased on the use of the schemes developed for controlled nonlinear multiphoton\nabsorption of light by quantum systems. The relation between the field of\nquantum neural computing and quantum control is discussed.",
    "arxiv_id": "quant-ph/0105134",
    "authors": [
      "A. A. Ezhov",
      "A. G. Khromov",
      "G. P. Berman"
    ],
    "categories": [
      "quant-ph"
    ],
    "title": "Multivariable functions approximation using a single quantum neuron",
    "url": "https://arxiv.org/abs/quant-ph/0105134"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "cba96ac5-75b1-422b-9cfc-81e2c507862f",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}