{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:CIKVEEGPT7LSF4JYLMBDWVFZYI","short_pith_number":"pith:CIKVEEGP","schema_version":"1.0","canonical_sha256":"12155210cf9fd722f1385b023b54b9c2226b9ccaf0a5d3787d75a066e6e09bdd","source":{"kind":"arxiv","id":"2607.26349","version":1},"attestation_state":"computed","paper":{"title":"Hardware-Aware QUBO Reformulation of Constrained Binary Optimization via the Walsh-Fourier Transform","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"quant-ph","authors_text":"Harsha Nagarajan, Loong Kuan Lee, Nico Piatkowski, Ragavi Krishnamoorthy, Sascha M\\\"ucke, Thore Gerlach","submitted_at":"2026-07-28T23:45:18Z","abstract_excerpt":"We present a novel slack-free, penalty-based framework for reformulating constrained binary optimization as Quadratic Unconstrained Binary Optimization (QUBO) on near-term quantum annealing hardware. Given a user-chosen penalty function that most naturally captures a constraint---typically non-quadratic, such as a Heaviside-function surrogate---and a target probability measure over the Boolean hypercube, our method returns the weighted least-squares projection of the chosen penalty function onto the subspace spanned by linear and quadratic Walsh--Fourier characters that correspond to physicall"},"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":"2607.26349","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-07-28T23:45:18Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"f6b70151b82a07ebbaf550ec88d6a94b6c2ef8a78612ed4e787a03667e3aeb65","abstract_canon_sha256":"2a11b5efab22d2b2a2a1f62f4dfc6918a95dff906fe97510d4144a858f692142"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12155210cf9fd722f1385b023b54b9c2226b9ccaf0a5d3787d75a066e6e09bdd","last_reissued_at":"2026-07-30T01:18:08.454475Z","signature_status":"unsigned_v0","first_computed_at":"2026-07-30T01:18:08.454475Z"},"graph_snapshot":{"paper":{"title":"Hardware-Aware QUBO Reformulation of Constrained Binary Optimization via the Walsh-Fourier Transform","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"quant-ph","authors_text":"Harsha Nagarajan, Loong Kuan Lee, Nico Piatkowski, Ragavi Krishnamoorthy, Sascha M\\\"ucke, Thore Gerlach","submitted_at":"2026-07-28T23:45:18Z","abstract_excerpt":"We present a novel slack-free, penalty-based framework for reformulating constrained binary optimization as Quadratic Unconstrained Binary Optimization (QUBO) on near-term quantum annealing hardware. Given a user-chosen penalty function that most naturally captures a constraint---typically non-quadratic, such as a Heaviside-function surrogate---and a target probability measure over the Boolean hypercube, our method returns the weighted least-squares projection of the chosen penalty function onto the subspace spanned by linear and quadratic Walsh--Fourier characters that correspond to physicall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.26349","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/2607.26349/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":"2607.26349","created_at":"2026-07-30T01:18:08.459537+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.26349v1","created_at":"2026-07-30T01:18:08.459537+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.26349","created_at":"2026-07-30T01:18:08.459537+00:00"},{"alias_kind":"pith_short_12","alias_value":"CIKVEEGPT7LS","created_at":"2026-07-30T01:18:08.459537+00:00"},{"alias_kind":"pith_short_16","alias_value":"CIKVEEGPT7LSF4JY","created_at":"2026-07-30T01:18:08.459537+00:00"},{"alias_kind":"pith_short_8","alias_value":"CIKVEEGP","created_at":"2026-07-30T01:18:08.459537+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/CIKVEEGPT7LSF4JYLMBDWVFZYI","json":"https://pith.science/pith/CIKVEEGPT7LSF4JYLMBDWVFZYI.json","graph_json":"https://pith.science/api/pith-number/CIKVEEGPT7LSF4JYLMBDWVFZYI/graph.json","events_json":"https://pith.science/api/pith-number/CIKVEEGPT7LSF4JYLMBDWVFZYI/events.json","paper":"https://pith.science/paper/CIKVEEGP"},"agent_actions":{"view_html":"https://pith.science/pith/CIKVEEGPT7LSF4JYLMBDWVFZYI","download_json":"https://pith.science/pith/CIKVEEGPT7LSF4JYLMBDWVFZYI.json","view_paper":"https://pith.science/paper/CIKVEEGP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.26349&json=true","fetch_graph":"https://pith.science/api/pith-number/CIKVEEGPT7LSF4JYLMBDWVFZYI/graph.json","fetch_events":"https://pith.science/api/pith-number/CIKVEEGPT7LSF4JYLMBDWVFZYI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CIKVEEGPT7LSF4JYLMBDWVFZYI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CIKVEEGPT7LSF4JYLMBDWVFZYI/action/storage_attestation","attest_author":"https://pith.science/pith/CIKVEEGPT7LSF4JYLMBDWVFZYI/action/author_attestation","sign_citation":"https://pith.science/pith/CIKVEEGPT7LSF4JYLMBDWVFZYI/action/citation_signature","submit_replication":"https://pith.science/pith/CIKVEEGPT7LSF4JYLMBDWVFZYI/action/replication_record"}},"created_at":"2026-07-30T01:18:08.459537+00:00","updated_at":"2026-07-30T01:18:08.459537+00:00"}