{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5ZBJ7RSPJZETQ6GGSCYAOCFQBV","short_pith_number":"pith:5ZBJ7RSP","schema_version":"1.0","canonical_sha256":"ee429fc64f4e493878c690b00708b00d57ed5e5d2a4ee55512821ae4ae38af3a","source":{"kind":"arxiv","id":"2407.21641","version":4},"attestation_state":"computed","paper":{"title":"Enhancing the Harrow-Hassidim-Lloyd (HHL) algorithm in systems with large condition numbers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.chem-ph","quant-ph"],"primary_cat":"physics.atom-ph","authors_text":"Akshaya Jayashankar, K. Sugisaki, Nishanth Baskaran, Peniel Bertrand Tsemo, Sayan Chakraborty, V. S. Prasannaa","submitted_at":"2024-07-31T14:41:30Z","abstract_excerpt":"Although the Harrow-Hassidim-Lloyd (HHL) algorithm offers an exponential speedup in system size for treating linear equations of the form $A\\vec{x}=\\vec{b}$ on quantum computers when compared to their traditional counterparts, it faces a challenge related to the condition number ($\\mathcal{\\kappa}$) scaling of the $A$ matrix. In this work, we address the issue by introducing the post-selection-improved HHL (Psi-HHL) framework that operates on a simple yet effective premise: subtracting mixed and wrong signals to extract correct signals while providing the benefit of optimal scaling in the cond"},"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":"2407.21641","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.atom-ph","submitted_at":"2024-07-31T14:41:30Z","cross_cats_sorted":["physics.chem-ph","quant-ph"],"title_canon_sha256":"f9d161a7b7a97952ec870c9036e3d775de5f4267498c34902d2de259a2311ea6","abstract_canon_sha256":"79cdb6e7a3b1439f34cb2e3eb4c02bf487844978c4b23598be578cf44aa1dec0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:59.054294Z","signature_b64":"5oQjYzyGh8sRjTGE5fK64oiLpXOe2w4c741VSfDDgWa8JNqYiNte1FvFOgGp3FPIGzlUvvaWMs4X/i6MIbggBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee429fc64f4e493878c690b00708b00d57ed5e5d2a4ee55512821ae4ae38af3a","last_reissued_at":"2026-07-05T11:08:59.053802Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:59.053802Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing the Harrow-Hassidim-Lloyd (HHL) algorithm in systems with large condition numbers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.chem-ph","quant-ph"],"primary_cat":"physics.atom-ph","authors_text":"Akshaya Jayashankar, K. Sugisaki, Nishanth Baskaran, Peniel Bertrand Tsemo, Sayan Chakraborty, V. S. Prasannaa","submitted_at":"2024-07-31T14:41:30Z","abstract_excerpt":"Although the Harrow-Hassidim-Lloyd (HHL) algorithm offers an exponential speedup in system size for treating linear equations of the form $A\\vec{x}=\\vec{b}$ on quantum computers when compared to their traditional counterparts, it faces a challenge related to the condition number ($\\mathcal{\\kappa}$) scaling of the $A$ matrix. In this work, we address the issue by introducing the post-selection-improved HHL (Psi-HHL) framework that operates on a simple yet effective premise: subtracting mixed and wrong signals to extract correct signals while providing the benefit of optimal scaling in the cond"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.21641","kind":"arxiv","version":4},"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/2407.21641/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":"2407.21641","created_at":"2026-07-05T11:08:59.053864+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.21641v4","created_at":"2026-07-05T11:08:59.053864+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.21641","created_at":"2026-07-05T11:08:59.053864+00:00"},{"alias_kind":"pith_short_12","alias_value":"5ZBJ7RSPJZET","created_at":"2026-07-05T11:08:59.053864+00:00"},{"alias_kind":"pith_short_16","alias_value":"5ZBJ7RSPJZETQ6GG","created_at":"2026-07-05T11:08:59.053864+00:00"},{"alias_kind":"pith_short_8","alias_value":"5ZBJ7RSP","created_at":"2026-07-05T11:08:59.053864+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.24694","citing_title":"Encoding strategies for quantum enhanced fluid simulations: opportunities and challenges","ref_index":47,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5ZBJ7RSPJZETQ6GGSCYAOCFQBV","json":"https://pith.science/pith/5ZBJ7RSPJZETQ6GGSCYAOCFQBV.json","graph_json":"https://pith.science/api/pith-number/5ZBJ7RSPJZETQ6GGSCYAOCFQBV/graph.json","events_json":"https://pith.science/api/pith-number/5ZBJ7RSPJZETQ6GGSCYAOCFQBV/events.json","paper":"https://pith.science/paper/5ZBJ7RSP"},"agent_actions":{"view_html":"https://pith.science/pith/5ZBJ7RSPJZETQ6GGSCYAOCFQBV","download_json":"https://pith.science/pith/5ZBJ7RSPJZETQ6GGSCYAOCFQBV.json","view_paper":"https://pith.science/paper/5ZBJ7RSP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.21641&json=true","fetch_graph":"https://pith.science/api/pith-number/5ZBJ7RSPJZETQ6GGSCYAOCFQBV/graph.json","fetch_events":"https://pith.science/api/pith-number/5ZBJ7RSPJZETQ6GGSCYAOCFQBV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5ZBJ7RSPJZETQ6GGSCYAOCFQBV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5ZBJ7RSPJZETQ6GGSCYAOCFQBV/action/storage_attestation","attest_author":"https://pith.science/pith/5ZBJ7RSPJZETQ6GGSCYAOCFQBV/action/author_attestation","sign_citation":"https://pith.science/pith/5ZBJ7RSPJZETQ6GGSCYAOCFQBV/action/citation_signature","submit_replication":"https://pith.science/pith/5ZBJ7RSPJZETQ6GGSCYAOCFQBV/action/replication_record"}},"created_at":"2026-07-05T11:08:59.053864+00:00","updated_at":"2026-07-05T11:08:59.053864+00:00"}