{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CYE3VZ3U3R37JFXZKD22WI7IEL","short_pith_number":"pith:CYE3VZ3U","schema_version":"1.0","canonical_sha256":"1609bae774dc77f496f950f5ab23e822e83852114881133abf62446af7bc0167","source":{"kind":"arxiv","id":"2109.04248","version":1},"attestation_state":"computed","paper":{"title":"Improving quantum linear system solvers via a gradient descent perspective","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"quant-ph","authors_text":"D\\'aniel Szil\\'agyi, Iordanis Kerenidis, Sander Gribling","submitted_at":"2021-09-09T13:16:28Z","abstract_excerpt":"Solving systems of linear equations is one of the most important primitives in quantum computing that has the potential to provide a practical quantum advantage in many different areas, including in optimization, simulation, and machine learning. In this work, we revisit quantum linear system solvers from the perspective of convex optimization, and in particular gradient descent-type algorithms. This leads to a considerable constant-factor improvement in the runtime (or, conversely, a several orders of magnitude smaller error with the same runtime/circuit depth).\n  More precisely, we first sho"},"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":"2109.04248","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-09-09T13:16:28Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"dc4cdcc82c29a01c74cccafce380ee63f4106101d28f65450136d3973926700e","abstract_canon_sha256":"16556bb209ba2372fe8d8f98c33707f69dd7f6270fa495f14beba3da9b1c1321"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:12:56.646072Z","signature_b64":"IVXaVQ6h9MTVSe+h3eUv7a2Vucetogyxikcee+oTK2KowjIwOmNUjtryvBrIejJ5L3bljlJWMJ9ZcDoOXbpxBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1609bae774dc77f496f950f5ab23e822e83852114881133abf62446af7bc0167","last_reissued_at":"2026-07-05T03:12:56.645716Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:12:56.645716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving quantum linear system solvers via a gradient descent perspective","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"quant-ph","authors_text":"D\\'aniel Szil\\'agyi, Iordanis Kerenidis, Sander Gribling","submitted_at":"2021-09-09T13:16:28Z","abstract_excerpt":"Solving systems of linear equations is one of the most important primitives in quantum computing that has the potential to provide a practical quantum advantage in many different areas, including in optimization, simulation, and machine learning. In this work, we revisit quantum linear system solvers from the perspective of convex optimization, and in particular gradient descent-type algorithms. This leads to a considerable constant-factor improvement in the runtime (or, conversely, a several orders of magnitude smaller error with the same runtime/circuit depth).\n  More precisely, we first sho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.04248","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/2109.04248/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":"2109.04248","created_at":"2026-07-05T03:12:56.645788+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.04248v1","created_at":"2026-07-05T03:12:56.645788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.04248","created_at":"2026-07-05T03:12:56.645788+00:00"},{"alias_kind":"pith_short_12","alias_value":"CYE3VZ3U3R37","created_at":"2026-07-05T03:12:56.645788+00:00"},{"alias_kind":"pith_short_16","alias_value":"CYE3VZ3U3R37JFXZ","created_at":"2026-07-05T03:12:56.645788+00:00"},{"alias_kind":"pith_short_8","alias_value":"CYE3VZ3U","created_at":"2026-07-05T03:12:56.645788+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.15537","citing_title":"Matrix inversion polynomials for the quantum singular value transformation","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CYE3VZ3U3R37JFXZKD22WI7IEL","json":"https://pith.science/pith/CYE3VZ3U3R37JFXZKD22WI7IEL.json","graph_json":"https://pith.science/api/pith-number/CYE3VZ3U3R37JFXZKD22WI7IEL/graph.json","events_json":"https://pith.science/api/pith-number/CYE3VZ3U3R37JFXZKD22WI7IEL/events.json","paper":"https://pith.science/paper/CYE3VZ3U"},"agent_actions":{"view_html":"https://pith.science/pith/CYE3VZ3U3R37JFXZKD22WI7IEL","download_json":"https://pith.science/pith/CYE3VZ3U3R37JFXZKD22WI7IEL.json","view_paper":"https://pith.science/paper/CYE3VZ3U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.04248&json=true","fetch_graph":"https://pith.science/api/pith-number/CYE3VZ3U3R37JFXZKD22WI7IEL/graph.json","fetch_events":"https://pith.science/api/pith-number/CYE3VZ3U3R37JFXZKD22WI7IEL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CYE3VZ3U3R37JFXZKD22WI7IEL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CYE3VZ3U3R37JFXZKD22WI7IEL/action/storage_attestation","attest_author":"https://pith.science/pith/CYE3VZ3U3R37JFXZKD22WI7IEL/action/author_attestation","sign_citation":"https://pith.science/pith/CYE3VZ3U3R37JFXZKD22WI7IEL/action/citation_signature","submit_replication":"https://pith.science/pith/CYE3VZ3U3R37JFXZKD22WI7IEL/action/replication_record"}},"created_at":"2026-07-05T03:12:56.645788+00:00","updated_at":"2026-07-05T03:12:56.645788+00:00"}