{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:KZKN3LLASC6L32MKZFNWZFPAQ7","short_pith_number":"pith:KZKN3LLA","schema_version":"1.0","canonical_sha256":"5654ddad6090bcbde98ac95b6c95e087fb6fb88f6d8ce3f2e10652c4d8af0f1f","source":{"kind":"arxiv","id":"2311.12929","version":1},"attestation_state":"computed","paper":{"title":"Hierarchical Learning for Quantum ML: Novel Training Technique for Large-Scale Variational Quantum Circuits","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Hayk Tepanyan, Hrant Gharibyan, Vincent Su","submitted_at":"2023-11-21T19:00:03Z","abstract_excerpt":"We present hierarchical learning, a novel variational architecture for efficient training of large-scale variational quantum circuits. We test and benchmark our technique for distribution loading with quantum circuit born machines (QCBMs). With QCBMs, probability distributions are loaded into the squared amplitudes of computational basis vectors represented by bitstrings. Our key insight is to take advantage of the fact that the most significant (qu)bits have a greater effect on the final distribution and can be learned first. One can think of it as a generalization of layerwise learning, wher"},"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":"2311.12929","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2023-11-21T19:00:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2907913ce4c837ee1fdfe9f4323669376f18a7b6202cc907419d373a180f0358","abstract_canon_sha256":"72101bf9e942f0c79f55f743bfce674e307d70326e9477c334c405ad0167a80a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:15:20.607628Z","signature_b64":"MXcZZlXXyObeJfn48EU3RMUSSZlWHDayI2U7RTKlB35L1Coazzjrys3VtgaOze9nkIzlMp3ayNqjXhuDyDcgDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5654ddad6090bcbde98ac95b6c95e087fb6fb88f6d8ce3f2e10652c4d8af0f1f","last_reissued_at":"2026-07-05T07:15:20.607059Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:15:20.607059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hierarchical Learning for Quantum ML: Novel Training Technique for Large-Scale Variational Quantum Circuits","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Hayk Tepanyan, Hrant Gharibyan, Vincent Su","submitted_at":"2023-11-21T19:00:03Z","abstract_excerpt":"We present hierarchical learning, a novel variational architecture for efficient training of large-scale variational quantum circuits. We test and benchmark our technique for distribution loading with quantum circuit born machines (QCBMs). With QCBMs, probability distributions are loaded into the squared amplitudes of computational basis vectors represented by bitstrings. Our key insight is to take advantage of the fact that the most significant (qu)bits have a greater effect on the final distribution and can be learned first. One can think of it as a generalization of layerwise learning, wher"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.12929","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/2311.12929/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":"2311.12929","created_at":"2026-07-05T07:15:20.607119+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.12929v1","created_at":"2026-07-05T07:15:20.607119+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.12929","created_at":"2026-07-05T07:15:20.607119+00:00"},{"alias_kind":"pith_short_12","alias_value":"KZKN3LLASC6L","created_at":"2026-07-05T07:15:20.607119+00:00"},{"alias_kind":"pith_short_16","alias_value":"KZKN3LLASC6L32MK","created_at":"2026-07-05T07:15:20.607119+00:00"},{"alias_kind":"pith_short_8","alias_value":"KZKN3LLA","created_at":"2026-07-05T07:15:20.607119+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01291","citing_title":"Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework","ref_index":148,"is_internal_anchor":false},{"citing_arxiv_id":"2507.01246","citing_title":"Quantum Machine Learning for State Tomography Using Classical Data","ref_index":112,"is_internal_anchor":false},{"citing_arxiv_id":"2604.26213","citing_title":"Qvine: Vine Structured Quantum Circuits for Loading High Dimensional Distributions","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KZKN3LLASC6L32MKZFNWZFPAQ7","json":"https://pith.science/pith/KZKN3LLASC6L32MKZFNWZFPAQ7.json","graph_json":"https://pith.science/api/pith-number/KZKN3LLASC6L32MKZFNWZFPAQ7/graph.json","events_json":"https://pith.science/api/pith-number/KZKN3LLASC6L32MKZFNWZFPAQ7/events.json","paper":"https://pith.science/paper/KZKN3LLA"},"agent_actions":{"view_html":"https://pith.science/pith/KZKN3LLASC6L32MKZFNWZFPAQ7","download_json":"https://pith.science/pith/KZKN3LLASC6L32MKZFNWZFPAQ7.json","view_paper":"https://pith.science/paper/KZKN3LLA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.12929&json=true","fetch_graph":"https://pith.science/api/pith-number/KZKN3LLASC6L32MKZFNWZFPAQ7/graph.json","fetch_events":"https://pith.science/api/pith-number/KZKN3LLASC6L32MKZFNWZFPAQ7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KZKN3LLASC6L32MKZFNWZFPAQ7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KZKN3LLASC6L32MKZFNWZFPAQ7/action/storage_attestation","attest_author":"https://pith.science/pith/KZKN3LLASC6L32MKZFNWZFPAQ7/action/author_attestation","sign_citation":"https://pith.science/pith/KZKN3LLASC6L32MKZFNWZFPAQ7/action/citation_signature","submit_replication":"https://pith.science/pith/KZKN3LLASC6L32MKZFNWZFPAQ7/action/replication_record"}},"created_at":"2026-07-05T07:15:20.607119+00:00","updated_at":"2026-07-05T07:15:20.607119+00:00"}