{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:YCQKG6LHKCXF6B6KCDKD32SWET","short_pith_number":"pith:YCQKG6LH","schema_version":"1.0","canonical_sha256":"c0a0a3796750ae5f07ca10d43dea5624f52ca014b314162b52edffa785640190","source":{"kind":"arxiv","id":"2106.00065","version":1},"attestation_state":"computed","paper":{"title":"Using machine learning for quantum annealing accuracy prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.ET","cs.LG"],"primary_cat":"quant-ph","authors_text":"Aaron Barbosa, Elijah Pelofske, Georg Hahn, Hristo N. Djidjev","submitted_at":"2021-05-31T19:14:37Z","abstract_excerpt":"Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or QUBO (quadratic unconstrained binary optimization) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer fo"},"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":"2106.00065","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2021-05-31T19:14:37Z","cross_cats_sorted":["cs.ET","cs.LG"],"title_canon_sha256":"743fec19cb7ac3d9624c4b104342dbbb8ed77b7235d9fab7a920ef0485a9eecb","abstract_canon_sha256":"0609d910eadae5c1bbd40e7b4c453f13daca7d87bef4a5045e4de5210dce1319"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:10:34.461242Z","signature_b64":"0qXGGBTjj60fATDiWKaPO1Jl8EASwQqxOFGkWyfdimo/k8oJvq5COvQOwCVIYe993MOBnJCYCSRzr/gAXyLsBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c0a0a3796750ae5f07ca10d43dea5624f52ca014b314162b52edffa785640190","last_reissued_at":"2026-07-05T05:10:34.460836Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:10:34.460836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using machine learning for quantum annealing accuracy prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.ET","cs.LG"],"primary_cat":"quant-ph","authors_text":"Aaron Barbosa, Elijah Pelofske, Georg Hahn, Hristo N. Djidjev","submitted_at":"2021-05-31T19:14:37Z","abstract_excerpt":"Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or QUBO (quadratic unconstrained binary optimization) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.00065","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/2106.00065/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":"2106.00065","created_at":"2026-07-05T05:10:34.460895+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.00065v1","created_at":"2026-07-05T05:10:34.460895+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.00065","created_at":"2026-07-05T05:10:34.460895+00:00"},{"alias_kind":"pith_short_12","alias_value":"YCQKG6LHKCXF","created_at":"2026-07-05T05:10:34.460895+00:00"},{"alias_kind":"pith_short_16","alias_value":"YCQKG6LHKCXF6B6K","created_at":"2026-07-05T05:10:34.460895+00:00"},{"alias_kind":"pith_short_8","alias_value":"YCQKG6LH","created_at":"2026-07-05T05:10:34.460895+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/YCQKG6LHKCXF6B6KCDKD32SWET","json":"https://pith.science/pith/YCQKG6LHKCXF6B6KCDKD32SWET.json","graph_json":"https://pith.science/api/pith-number/YCQKG6LHKCXF6B6KCDKD32SWET/graph.json","events_json":"https://pith.science/api/pith-number/YCQKG6LHKCXF6B6KCDKD32SWET/events.json","paper":"https://pith.science/paper/YCQKG6LH"},"agent_actions":{"view_html":"https://pith.science/pith/YCQKG6LHKCXF6B6KCDKD32SWET","download_json":"https://pith.science/pith/YCQKG6LHKCXF6B6KCDKD32SWET.json","view_paper":"https://pith.science/paper/YCQKG6LH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.00065&json=true","fetch_graph":"https://pith.science/api/pith-number/YCQKG6LHKCXF6B6KCDKD32SWET/graph.json","fetch_events":"https://pith.science/api/pith-number/YCQKG6LHKCXF6B6KCDKD32SWET/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YCQKG6LHKCXF6B6KCDKD32SWET/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YCQKG6LHKCXF6B6KCDKD32SWET/action/storage_attestation","attest_author":"https://pith.science/pith/YCQKG6LHKCXF6B6KCDKD32SWET/action/author_attestation","sign_citation":"https://pith.science/pith/YCQKG6LHKCXF6B6KCDKD32SWET/action/citation_signature","submit_replication":"https://pith.science/pith/YCQKG6LHKCXF6B6KCDKD32SWET/action/replication_record"}},"created_at":"2026-07-05T05:10:34.460895+00:00","updated_at":"2026-07-05T05:10:34.460895+00:00"}