{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:57VLGQ2KLFEIUAGJGIYTZLJELF","short_pith_number":"pith:57VLGQ2K","schema_version":"1.0","canonical_sha256":"efeab3434a59488a00c932313cad24595613395169cac47886b040bd109c9711","source":{"kind":"arxiv","id":"2210.06539","version":1},"attestation_state":"computed","paper":{"title":"Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"quant-ph","authors_text":"Andrew M. Childs, Chunhao Wang, Jin-Peng Liu, Ruizhe Zhang, Tongyang Li","submitted_at":"2022-10-12T19:10:43Z","abstract_excerpt":"Given a convex function $f\\colon\\mathbb{R}^{d}\\to\\mathbb{R}$, the problem of sampling from a distribution $\\propto e^{-f(x)}$ is called log-concave sampling. This task has wide applications in machine learning, physics, statistics, etc. In this work, we develop quantum algorithms for sampling log-concave distributions and for estimating their normalizing constants $\\int_{\\mathbb{R}^d}e^{-f(x)}\\mathrm{d} x$. First, we use underdamped Langevin diffusion to develop quantum algorithms that match the query complexity (in terms of the condition number $\\kappa$ and dimension $d$) of analogous classic"},"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":"2210.06539","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2022-10-12T19:10:43Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"cbf9138b15bb4305721a1c1f1f101da10fba655bc3c77f144aec7d48cd640d2c","abstract_canon_sha256":"485e91c689f68bb80259dc26be99630c3022ffbc0a64c1b19bfaf67880a243d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:21:39.939371Z","signature_b64":"i0SCk553MdSIZagvxHlEAkceMrLh29QMeCtVGpxuNdQPl8Lgs+xjJfZPShsMPPon6WRCn/+ouY2o7JPKOsisBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"efeab3434a59488a00c932313cad24595613395169cac47886b040bd109c9711","last_reissued_at":"2026-07-05T07:21:39.938869Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:21:39.938869Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"quant-ph","authors_text":"Andrew M. Childs, Chunhao Wang, Jin-Peng Liu, Ruizhe Zhang, Tongyang Li","submitted_at":"2022-10-12T19:10:43Z","abstract_excerpt":"Given a convex function $f\\colon\\mathbb{R}^{d}\\to\\mathbb{R}$, the problem of sampling from a distribution $\\propto e^{-f(x)}$ is called log-concave sampling. This task has wide applications in machine learning, physics, statistics, etc. In this work, we develop quantum algorithms for sampling log-concave distributions and for estimating their normalizing constants $\\int_{\\mathbb{R}^d}e^{-f(x)}\\mathrm{d} x$. First, we use underdamped Langevin diffusion to develop quantum algorithms that match the query complexity (in terms of the condition number $\\kappa$ and dimension $d$) of analogous classic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.06539","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/2210.06539/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":"2210.06539","created_at":"2026-07-05T07:21:39.938923+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.06539v1","created_at":"2026-07-05T07:21:39.938923+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.06539","created_at":"2026-07-05T07:21:39.938923+00:00"},{"alias_kind":"pith_short_12","alias_value":"57VLGQ2KLFEI","created_at":"2026-07-05T07:21:39.938923+00:00"},{"alias_kind":"pith_short_16","alias_value":"57VLGQ2KLFEIUAGJ","created_at":"2026-07-05T07:21:39.938923+00:00"},{"alias_kind":"pith_short_8","alias_value":"57VLGQ2K","created_at":"2026-07-05T07:21:39.938923+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/57VLGQ2KLFEIUAGJGIYTZLJELF","json":"https://pith.science/pith/57VLGQ2KLFEIUAGJGIYTZLJELF.json","graph_json":"https://pith.science/api/pith-number/57VLGQ2KLFEIUAGJGIYTZLJELF/graph.json","events_json":"https://pith.science/api/pith-number/57VLGQ2KLFEIUAGJGIYTZLJELF/events.json","paper":"https://pith.science/paper/57VLGQ2K"},"agent_actions":{"view_html":"https://pith.science/pith/57VLGQ2KLFEIUAGJGIYTZLJELF","download_json":"https://pith.science/pith/57VLGQ2KLFEIUAGJGIYTZLJELF.json","view_paper":"https://pith.science/paper/57VLGQ2K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.06539&json=true","fetch_graph":"https://pith.science/api/pith-number/57VLGQ2KLFEIUAGJGIYTZLJELF/graph.json","fetch_events":"https://pith.science/api/pith-number/57VLGQ2KLFEIUAGJGIYTZLJELF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/57VLGQ2KLFEIUAGJGIYTZLJELF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/57VLGQ2KLFEIUAGJGIYTZLJELF/action/storage_attestation","attest_author":"https://pith.science/pith/57VLGQ2KLFEIUAGJGIYTZLJELF/action/author_attestation","sign_citation":"https://pith.science/pith/57VLGQ2KLFEIUAGJGIYTZLJELF/action/citation_signature","submit_replication":"https://pith.science/pith/57VLGQ2KLFEIUAGJGIYTZLJELF/action/replication_record"}},"created_at":"2026-07-05T07:21:39.938923+00:00","updated_at":"2026-07-05T07:21:39.938923+00:00"}