{
  "annotation_id": "2338b316-fd5f-4e04-b3df-025c807bea62",
  "date_created": "2026-03-02T18:02:44.585000Z",
  "date_modified": "2026-03-02T18:02:44.585000Z",
  "file_hash": "2f92a53012dc3210e3416ae0ab5bbb1393137eb86595bcc436dbe1b3e7c92f6f",
  "private": false,
  "record": {
    "abstract": "This paper combines quantum computation with classical neural network theory\nto produce a quantum computational learning algorithm. Quantum computation uses\nmicroscopic quantum level effects to perform computational tasks and has\nproduced results that in some cases are exponentially faster than their\nclassical counterparts. The unique characteristics of quantum theory may also\nbe used to create a quantum associative memory with a capacity exponential in\nthe number of neurons. This paper combines two quantum computational algorithms\nto produce such a quantum associative memory. The result is an exponential\nincrease in the capacity of the memory when compared to traditional associative\nmemories such as the Hopfield network. The paper covers necessary high-level\nquantum mechanical and quantum computational ideas and introduces a quantum\nassociative memory. Theoretical analysis proves the utility of the memory, and\nit is noted that a small version should be physically realizable in the near\nfuture.",
    "arxiv_id": "quant-ph/9807053",
    "authors": [
      "Dan Ventura",
      "Tony Martinez"
    ],
    "categories": [
      "quant-ph"
    ],
    "title": "Quantum Associative Memory",
    "url": "https://arxiv.org/abs/quant-ph/9807053"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "9a2c6379-c2a2-43c0-a9d7-56b9c9f5374e",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}