{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:1998:O6AELHLUS76MZJU4HXCPUJYOIF","short_pith_number":"pith:O6AELHLU","schema_version":"1.0","canonical_sha256":"7780459d7497fccca69c3dc4fa270e4164b685d1e2fab64f1d01fb08fdc8dc6f","source":{"kind":"arxiv","id":"quant-ph/9807053","version":1},"attestation_state":"computed","paper":{"title":"Quantum Associative Memory","license":"","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Dan Ventura, Tony Martinez","submitted_at":"1998-07-18T23:00:18Z","abstract_excerpt":"This paper combines quantum computation with classical neural network theory to produce a quantum computational learning algorithm. Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts. The unique characteristics of quantum theory may also be used to create a quantum associative memory with a capacity exponential in the number of neurons. This paper combines two quantum computational algorithms to produce such a quantum associative memory. The result is an"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"quant-ph/9807053","kind":"arxiv","version":1},"metadata":{"license":"","primary_cat":"quant-ph","submitted_at":"1998-07-18T23:00:18Z","cross_cats_sorted":[],"title_canon_sha256":"1c71776db0617ba538a84c35788558120f04e042fb9daa4105b6d99352c48b55","abstract_canon_sha256":"8ad465dedc5ae9a637e720a6b41e18ec63c53c1e61b571f6975ff554f22c4980"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T14:47:13.505205Z","signature_b64":"XJHgbKt1Cq5do56407CSWVVECQmIXoRnMrH++cGav2tumRCutHB7YBPyjX/oPgenO6uE/Zn706465UgXFZNdDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7780459d7497fccca69c3dc4fa270e4164b685d1e2fab64f1d01fb08fdc8dc6f","last_reissued_at":"2026-07-04T14:47:13.504742Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T14:47:13.504742Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Associative Memory","license":"","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Dan Ventura, Tony Martinez","submitted_at":"1998-07-18T23:00:18Z","abstract_excerpt":"This paper combines quantum computation with classical neural network theory to produce a quantum computational learning algorithm. Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts. The unique characteristics of quantum theory may also be used to create a quantum associative memory with a capacity exponential in the number of neurons. This paper combines two quantum computational algorithms to produce such a quantum associative memory. The result is an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"quant-ph/9807053","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/quant-ph/9807053/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"quant-ph/9807053","created_at":"2026-07-04T14:47:13.504805+00:00"},{"alias_kind":"arxiv_version","alias_value":"quant-ph/9807053v1","created_at":"2026-07-04T14:47:13.504805+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.quant-ph/9807053","created_at":"2026-07-04T14:47:13.504805+00:00"},{"alias_kind":"pith_short_12","alias_value":"O6AELHLUS76M","created_at":"2026-07-04T14:47:13.504805+00:00"},{"alias_kind":"pith_short_16","alias_value":"O6AELHLUS76MZJU4","created_at":"2026-07-04T14:47:13.504805+00:00"},{"alias_kind":"pith_short_8","alias_value":"O6AELHLU","created_at":"2026-07-04T14:47:13.504805+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.08433","citing_title":"The Input Problem: A Permanent Bottleneck for Quantum Machine Learning","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O6AELHLUS76MZJU4HXCPUJYOIF","json":"https://pith.science/pith/O6AELHLUS76MZJU4HXCPUJYOIF.json","graph_json":"https://pith.science/api/pith-number/O6AELHLUS76MZJU4HXCPUJYOIF/graph.json","events_json":"https://pith.science/api/pith-number/O6AELHLUS76MZJU4HXCPUJYOIF/events.json","paper":"https://pith.science/paper/O6AELHLU"},"agent_actions":{"view_html":"https://pith.science/pith/O6AELHLUS76MZJU4HXCPUJYOIF","download_json":"https://pith.science/pith/O6AELHLUS76MZJU4HXCPUJYOIF.json","view_paper":"https://pith.science/paper/O6AELHLU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=quant-ph/9807053&json=true","fetch_graph":"https://pith.science/api/pith-number/O6AELHLUS76MZJU4HXCPUJYOIF/graph.json","fetch_events":"https://pith.science/api/pith-number/O6AELHLUS76MZJU4HXCPUJYOIF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O6AELHLUS76MZJU4HXCPUJYOIF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O6AELHLUS76MZJU4HXCPUJYOIF/action/storage_attestation","attest_author":"https://pith.science/pith/O6AELHLUS76MZJU4HXCPUJYOIF/action/author_attestation","sign_citation":"https://pith.science/pith/O6AELHLUS76MZJU4HXCPUJYOIF/action/citation_signature","submit_replication":"https://pith.science/pith/O6AELHLUS76MZJU4HXCPUJYOIF/action/replication_record"}},"created_at":"2026-07-04T14:47:13.504805+00:00","updated_at":"2026-07-04T14:47:13.504805+00:00"}