{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:JNK6IRDM7BJOJHXQ2YSPR2M3OH","short_pith_number":"pith:JNK6IRDM","schema_version":"1.0","canonical_sha256":"4b55e4446cf852e49ef0d624f8e99b71fb2f4d6ad6e7b844782fea93295e9cfc","source":{"kind":"arxiv","id":"1301.1132","version":4},"attestation_state":"computed","paper":{"title":"Strategy for quantum algorithm design assisted by machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Jeongho Bang, Jinhyoung Lee, Junghee Ryu, Marcin Pawlowski, Seokwon Yoo","submitted_at":"2013-01-07T09:17:08Z","abstract_excerpt":"We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a \"quantum student\" is being taught by a \"classical teacher.\" In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem assisted by classical main-feedback system. Our method is applicable to design quantum oracle-based algorithm. As a case study, we chose an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte-Carlo simulations that our simulator can faithfully learn q"},"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":"1301.1132","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2013-01-07T09:17:08Z","cross_cats_sorted":[],"title_canon_sha256":"1ff69524e64539f2172c3b9eb63806758db50dbe89a59969000bb4dabbc45c78","abstract_canon_sha256":"9e66d7ba673a79d68dfbf2c395cb7c77eac07b58c89a7ffe461fdcc8254706ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:47:24.463644Z","signature_b64":"3UMAtc/j9V0d8Mc84KSVifxzXcr52pM0bp5O7LOWc5mPlmeBv/7BZFaZpHjzb4eDoJu07W41nA9KEGj4gI49Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b55e4446cf852e49ef0d624f8e99b71fb2f4d6ad6e7b844782fea93295e9cfc","last_reissued_at":"2026-05-18T02:47:24.463285Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:47:24.463285Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Strategy for quantum algorithm design assisted by machine learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Jeongho Bang, Jinhyoung Lee, Junghee Ryu, Marcin Pawlowski, Seokwon Yoo","submitted_at":"2013-01-07T09:17:08Z","abstract_excerpt":"We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a \"quantum student\" is being taught by a \"classical teacher.\" In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem assisted by classical main-feedback system. Our method is applicable to design quantum oracle-based algorithm. As a case study, we chose an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte-Carlo simulations that our simulator can faithfully learn q"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.1132","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1301.1132","created_at":"2026-05-18T02:47:24.463347+00:00"},{"alias_kind":"arxiv_version","alias_value":"1301.1132v4","created_at":"2026-05-18T02:47:24.463347+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1301.1132","created_at":"2026-05-18T02:47:24.463347+00:00"},{"alias_kind":"pith_short_12","alias_value":"JNK6IRDM7BJO","created_at":"2026-05-18T12:27:49.015174+00:00"},{"alias_kind":"pith_short_16","alias_value":"JNK6IRDM7BJOJHXQ","created_at":"2026-05-18T12:27:49.015174+00:00"},{"alias_kind":"pith_short_8","alias_value":"JNK6IRDM","created_at":"2026-05-18T12:27:49.015174+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JNK6IRDM7BJOJHXQ2YSPR2M3OH","json":"https://pith.science/pith/JNK6IRDM7BJOJHXQ2YSPR2M3OH.json","graph_json":"https://pith.science/api/pith-number/JNK6IRDM7BJOJHXQ2YSPR2M3OH/graph.json","events_json":"https://pith.science/api/pith-number/JNK6IRDM7BJOJHXQ2YSPR2M3OH/events.json","paper":"https://pith.science/paper/JNK6IRDM"},"agent_actions":{"view_html":"https://pith.science/pith/JNK6IRDM7BJOJHXQ2YSPR2M3OH","download_json":"https://pith.science/pith/JNK6IRDM7BJOJHXQ2YSPR2M3OH.json","view_paper":"https://pith.science/paper/JNK6IRDM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1301.1132&json=true","fetch_graph":"https://pith.science/api/pith-number/JNK6IRDM7BJOJHXQ2YSPR2M3OH/graph.json","fetch_events":"https://pith.science/api/pith-number/JNK6IRDM7BJOJHXQ2YSPR2M3OH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JNK6IRDM7BJOJHXQ2YSPR2M3OH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JNK6IRDM7BJOJHXQ2YSPR2M3OH/action/storage_attestation","attest_author":"https://pith.science/pith/JNK6IRDM7BJOJHXQ2YSPR2M3OH/action/author_attestation","sign_citation":"https://pith.science/pith/JNK6IRDM7BJOJHXQ2YSPR2M3OH/action/citation_signature","submit_replication":"https://pith.science/pith/JNK6IRDM7BJOJHXQ2YSPR2M3OH/action/replication_record"}},"created_at":"2026-05-18T02:47:24.463347+00:00","updated_at":"2026-05-18T02:47:24.463347+00:00"}