{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:MDLHHX75C474CBUDR7X2TCO4YB","short_pith_number":"pith:MDLHHX75","schema_version":"1.0","canonical_sha256":"60d673dffd173fc106838fefa989dcc05b4f0bd6e2ebf4fc40a469a9c5dcb117","source":{"kind":"arxiv","id":"2005.10811","version":1},"attestation_state":"computed","paper":{"title":"A deep learning model for noise prediction on near-term quantum devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Alexander Zlokapa, Alexandru Gheorghiu","submitted_at":"2020-05-21T17:47:29Z","abstract_excerpt":"We present an approach for a deep-learning compiler of quantum circuits, designed to reduce the output noise of circuits run on a specific device. We train a convolutional neural network on experimental data from a quantum device to learn a hardware-specific noise model. A compiler then uses the trained network as a noise predictor and inserts sequences of gates in circuits so as to minimize expected noise. We tested this approach on the IBM 5-qubit devices and observed a reduction in output noise of 12.3% (95% CI [11.5%, 13.0%]) compared to the circuits obtained by the Qiskit compiler. Moreov"},"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":"2005.10811","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2020-05-21T17:47:29Z","cross_cats_sorted":[],"title_canon_sha256":"08b656e3203411728febab3d7f1363f1a949e6a4913f3c23ee644c20fd296eee","abstract_canon_sha256":"275196cbd7922848e98798ce6519b2d2ecb0fb1a310468b96dc9a1291ecba736"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:04:57.539450Z","signature_b64":"2gPTvHzEKqhUnua/hyUJxCXazM+Yn1FwTr1I3U4j1OmubJCka8E8gxOBEL9wlIzlHd551PlifDOITnzf6HQIBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60d673dffd173fc106838fefa989dcc05b4f0bd6e2ebf4fc40a469a9c5dcb117","last_reissued_at":"2026-07-05T01:04:57.538937Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:04:57.538937Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A deep learning model for noise prediction on near-term quantum devices","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Alexander Zlokapa, Alexandru Gheorghiu","submitted_at":"2020-05-21T17:47:29Z","abstract_excerpt":"We present an approach for a deep-learning compiler of quantum circuits, designed to reduce the output noise of circuits run on a specific device. We train a convolutional neural network on experimental data from a quantum device to learn a hardware-specific noise model. A compiler then uses the trained network as a noise predictor and inserts sequences of gates in circuits so as to minimize expected noise. We tested this approach on the IBM 5-qubit devices and observed a reduction in output noise of 12.3% (95% CI [11.5%, 13.0%]) compared to the circuits obtained by the Qiskit compiler. Moreov"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.10811","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/2005.10811/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":"2005.10811","created_at":"2026-07-05T01:04:57.538991+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.10811v1","created_at":"2026-07-05T01:04:57.538991+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.10811","created_at":"2026-07-05T01:04:57.538991+00:00"},{"alias_kind":"pith_short_12","alias_value":"MDLHHX75C474","created_at":"2026-07-05T01:04:57.538991+00:00"},{"alias_kind":"pith_short_16","alias_value":"MDLHHX75C474CBUD","created_at":"2026-07-05T01:04:57.538991+00:00"},{"alias_kind":"pith_short_8","alias_value":"MDLHHX75","created_at":"2026-07-05T01:04:57.538991+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2403.02294","citing_title":"Empirical learning of dynamical decoupling on quantum processors","ref_index":45,"is_internal_anchor":false},{"citing_arxiv_id":"2511.08267","citing_title":"Enhancing Circuit Fidelity in Transmon Qubit Rings via Operation Duration Tuning under Strong Connectivity Noise","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02066","citing_title":"Accelerating Noisy Variational Quantum Algorithms with Physics-Informed Denoising Networks","ref_index":52,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MDLHHX75C474CBUDR7X2TCO4YB","json":"https://pith.science/pith/MDLHHX75C474CBUDR7X2TCO4YB.json","graph_json":"https://pith.science/api/pith-number/MDLHHX75C474CBUDR7X2TCO4YB/graph.json","events_json":"https://pith.science/api/pith-number/MDLHHX75C474CBUDR7X2TCO4YB/events.json","paper":"https://pith.science/paper/MDLHHX75"},"agent_actions":{"view_html":"https://pith.science/pith/MDLHHX75C474CBUDR7X2TCO4YB","download_json":"https://pith.science/pith/MDLHHX75C474CBUDR7X2TCO4YB.json","view_paper":"https://pith.science/paper/MDLHHX75","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.10811&json=true","fetch_graph":"https://pith.science/api/pith-number/MDLHHX75C474CBUDR7X2TCO4YB/graph.json","fetch_events":"https://pith.science/api/pith-number/MDLHHX75C474CBUDR7X2TCO4YB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MDLHHX75C474CBUDR7X2TCO4YB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MDLHHX75C474CBUDR7X2TCO4YB/action/storage_attestation","attest_author":"https://pith.science/pith/MDLHHX75C474CBUDR7X2TCO4YB/action/author_attestation","sign_citation":"https://pith.science/pith/MDLHHX75C474CBUDR7X2TCO4YB/action/citation_signature","submit_replication":"https://pith.science/pith/MDLHHX75C474CBUDR7X2TCO4YB/action/replication_record"}},"created_at":"2026-07-05T01:04:57.538991+00:00","updated_at":"2026-07-05T01:04:57.538991+00:00"}