{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:H6CSW5DGEXSPHDPYJIKP52ISVA","short_pith_number":"pith:H6CSW5DG","schema_version":"1.0","canonical_sha256":"3f852b746625e4f38df84a14fee912a80b2a7102bba4362aca9dc06c98cb8752","source":{"kind":"arxiv","id":"1912.07636","version":1},"attestation_state":"computed","paper":{"title":"Scalable Bayesian Hamiltonian learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mes-hall","cond-mat.quant-gas","cond-mat.str-el"],"primary_cat":"quant-ph","authors_text":"Robin Harper, Steven T. Flammia, Tim J. Evans","submitted_at":"2019-12-16T19:04:28Z","abstract_excerpt":"As the size of quantum devices continues to grow, the development of scalable methods to characterise and diagnose noise is becoming an increasingly important problem. Recent methods have shown how to efficiently estimate Hamiltonians in principle, but they are poorly conditioned and can only characterize the system up to a scalar factor, making them difficult to use in practice. In this work we present a Bayesian methodology, called Bayesian Hamiltonian Learning (BHL), that addresses both of these issues by making use of any or all, of the following: well-characterised experimental control of"},"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":"1912.07636","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2019-12-16T19:04:28Z","cross_cats_sorted":["cond-mat.mes-hall","cond-mat.quant-gas","cond-mat.str-el"],"title_canon_sha256":"45bd08af6bec20e75016a4eb8cc38bf1a5a0b2d52fc0af5e66a394c04b60b745","abstract_canon_sha256":"673ef01f92f5c988e588abf8871378435a6c09ec0211d6f2e652b00ac253ced4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:26:24.512881Z","signature_b64":"pnPgNG75qIWcqe9Dz7FZjRPPOCbTa2jm+4UU0eQmDZJW8sLdd5OxKDqBg+ErvPSfqLji1B67hYSjHFkzh7pdCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f852b746625e4f38df84a14fee912a80b2a7102bba4362aca9dc06c98cb8752","last_reissued_at":"2026-07-05T00:26:24.512509Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:26:24.512509Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scalable Bayesian Hamiltonian learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.mes-hall","cond-mat.quant-gas","cond-mat.str-el"],"primary_cat":"quant-ph","authors_text":"Robin Harper, Steven T. Flammia, Tim J. Evans","submitted_at":"2019-12-16T19:04:28Z","abstract_excerpt":"As the size of quantum devices continues to grow, the development of scalable methods to characterise and diagnose noise is becoming an increasingly important problem. Recent methods have shown how to efficiently estimate Hamiltonians in principle, but they are poorly conditioned and can only characterize the system up to a scalar factor, making them difficult to use in practice. In this work we present a Bayesian methodology, called Bayesian Hamiltonian Learning (BHL), that addresses both of these issues by making use of any or all, of the following: well-characterised experimental control of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.07636","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/1912.07636/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":"1912.07636","created_at":"2026-07-05T00:26:24.512559+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.07636v1","created_at":"2026-07-05T00:26:24.512559+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.07636","created_at":"2026-07-05T00:26:24.512559+00:00"},{"alias_kind":"pith_short_12","alias_value":"H6CSW5DGEXSP","created_at":"2026-07-05T00:26:24.512559+00:00"},{"alias_kind":"pith_short_16","alias_value":"H6CSW5DGEXSPHDPY","created_at":"2026-07-05T00:26:24.512559+00:00"},{"alias_kind":"pith_short_8","alias_value":"H6CSW5DG","created_at":"2026-07-05T00:26:24.512559+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.20535","citing_title":"Near-Optimal Learning of Local Lindbladians","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2606.19486","citing_title":"Optimal Ansatz-free Hamiltonian Learning In Situ","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2606.20706","citing_title":"Efficient and SPAM-Robust Ansatz-Free Lindbladian Learning","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2606.05690","citing_title":"Learning Hamiltonians at Long Times","ref_index":7,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/H6CSW5DGEXSPHDPYJIKP52ISVA","json":"https://pith.science/pith/H6CSW5DGEXSPHDPYJIKP52ISVA.json","graph_json":"https://pith.science/api/pith-number/H6CSW5DGEXSPHDPYJIKP52ISVA/graph.json","events_json":"https://pith.science/api/pith-number/H6CSW5DGEXSPHDPYJIKP52ISVA/events.json","paper":"https://pith.science/paper/H6CSW5DG"},"agent_actions":{"view_html":"https://pith.science/pith/H6CSW5DGEXSPHDPYJIKP52ISVA","download_json":"https://pith.science/pith/H6CSW5DGEXSPHDPYJIKP52ISVA.json","view_paper":"https://pith.science/paper/H6CSW5DG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.07636&json=true","fetch_graph":"https://pith.science/api/pith-number/H6CSW5DGEXSPHDPYJIKP52ISVA/graph.json","fetch_events":"https://pith.science/api/pith-number/H6CSW5DGEXSPHDPYJIKP52ISVA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H6CSW5DGEXSPHDPYJIKP52ISVA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H6CSW5DGEXSPHDPYJIKP52ISVA/action/storage_attestation","attest_author":"https://pith.science/pith/H6CSW5DGEXSPHDPYJIKP52ISVA/action/author_attestation","sign_citation":"https://pith.science/pith/H6CSW5DGEXSPHDPYJIKP52ISVA/action/citation_signature","submit_replication":"https://pith.science/pith/H6CSW5DGEXSPHDPYJIKP52ISVA/action/replication_record"}},"created_at":"2026-07-05T00:26:24.512559+00:00","updated_at":"2026-07-05T00:26:24.512559+00:00"}