{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:E7U7BDE4MSF3PXKZTPPI64XW2G","short_pith_number":"pith:E7U7BDE4","schema_version":"1.0","canonical_sha256":"27e9f08c9c648bb7dd599bde8f72f6d1833fda911f3c36517686680c7bcff7be","source":{"kind":"arxiv","id":"2604.16527","version":2},"attestation_state":"computed","paper":{"title":"Late Breaking Results: Hardware-Aware Compilation Reshapes Trainability in Variational Quantum Circuits","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Transpilation reshapes gradient statistics and thus trainability in variational quantum circuits in an architecture-dependent way.","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Muhammad Kashif, Muhammad Shafique","submitted_at":"2026-04-16T14:08:02Z","abstract_excerpt":"Variational quantum circuits (VQCs) are typically evaluated at the logical design level when analyzing trainability. However, execution on real quantum devices requires hardware-aware compilation (transpilation) to satisfy qubit connectivity and native gate-set constraints. In this paper, we examine how transpilation can alter the gradient statistics. Using parameter-shift differentiation and gradient variance estimation, we compare logical and transpiled circuits across three representative ansatz families: EfficientSU2 (dense entanglement), TTN (tree tensor network), and RealAmplitudes (line"},"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":"2604.16527","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-04-16T14:08:02Z","cross_cats_sorted":[],"title_canon_sha256":"f1349f00b1a08ee2b3650dd08fa8398daac15e927e8c1ce239b6576870269417","abstract_canon_sha256":"ba9e25a9c831f92b9035e7dede96ae9dec03b86cacbca7189e222ac902c91a42"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:53.226440Z","signature_b64":"yXfb1bmULxD6fO7ifSS1vaC+MggN5mPjmUVNvLvb9EfTeo7CHdrUlYtpEBH0YGtdt1hDh9TMVspoxx8ObPzCDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"27e9f08c9c648bb7dd599bde8f72f6d1833fda911f3c36517686680c7bcff7be","last_reissued_at":"2026-06-02T02:04:53.226089Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:53.226089Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Late Breaking Results: Hardware-Aware Compilation Reshapes Trainability in Variational Quantum Circuits","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Transpilation reshapes gradient statistics and thus trainability in variational quantum circuits in an architecture-dependent way.","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Muhammad Kashif, Muhammad Shafique","submitted_at":"2026-04-16T14:08:02Z","abstract_excerpt":"Variational quantum circuits (VQCs) are typically evaluated at the logical design level when analyzing trainability. However, execution on real quantum devices requires hardware-aware compilation (transpilation) to satisfy qubit connectivity and native gate-set constraints. In this paper, we examine how transpilation can alter the gradient statistics. Using parameter-shift differentiation and gradient variance estimation, we compare logical and transpiled circuits across three representative ansatz families: EfficientSU2 (dense entanglement), TTN (tree tensor network), and RealAmplitudes (line"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"transpilation acts as an implicit structural transformation of the optimization landscape, motivating compilation-aware analysis and co-design for VQCs.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The differences in gradient statistics arise specifically from the structural changes imposed by satisfying hardware connectivity and gate-set constraints rather than from simulation artifacts or ansatz-specific choices.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Hardware-aware compilation reshapes gradient statistics and trainability of variational quantum circuits in an architecture-dependent manner.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Transpilation reshapes gradient statistics and thus trainability in variational quantum circuits in an architecture-dependent way.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"eb6d10ec8fe8ac3e6e8ccde6fd999f0068ed2febdc13eb24f5db9199f43ac0c1"},"source":{"id":"2604.16527","kind":"arxiv","version":2},"verdict":{"id":"452259d5-9b37-4b7d-a077-70693abcabed","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T11:40:42.957439Z","strongest_claim":"transpilation acts as an implicit structural transformation of the optimization landscape, motivating compilation-aware analysis and co-design for VQCs.","one_line_summary":"Hardware-aware compilation reshapes gradient statistics and trainability of variational quantum circuits in an architecture-dependent manner.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The differences in gradient statistics arise specifically from the structural changes imposed by satisfying hardware connectivity and gate-set constraints rather than from simulation artifacts or ansatz-specific choices.","pith_extraction_headline":"Transpilation reshapes gradient statistics and thus trainability in variational quantum circuits in an architecture-dependent way."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.16527/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":"2604.16527","created_at":"2026-06-02T02:04:53.226144+00:00"},{"alias_kind":"arxiv_version","alias_value":"2604.16527v2","created_at":"2026-06-02T02:04:53.226144+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.16527","created_at":"2026-06-02T02:04:53.226144+00:00"},{"alias_kind":"pith_short_12","alias_value":"E7U7BDE4MSF3","created_at":"2026-06-02T02:04:53.226144+00:00"},{"alias_kind":"pith_short_16","alias_value":"E7U7BDE4MSF3PXKZ","created_at":"2026-06-02T02:04:53.226144+00:00"},{"alias_kind":"pith_short_8","alias_value":"E7U7BDE4","created_at":"2026-06-02T02:04:53.226144+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.18345","citing_title":"Hybrid Quantum-Classical Neural Architecture Search","ref_index":58,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E7U7BDE4MSF3PXKZTPPI64XW2G","json":"https://pith.science/pith/E7U7BDE4MSF3PXKZTPPI64XW2G.json","graph_json":"https://pith.science/api/pith-number/E7U7BDE4MSF3PXKZTPPI64XW2G/graph.json","events_json":"https://pith.science/api/pith-number/E7U7BDE4MSF3PXKZTPPI64XW2G/events.json","paper":"https://pith.science/paper/E7U7BDE4"},"agent_actions":{"view_html":"https://pith.science/pith/E7U7BDE4MSF3PXKZTPPI64XW2G","download_json":"https://pith.science/pith/E7U7BDE4MSF3PXKZTPPI64XW2G.json","view_paper":"https://pith.science/paper/E7U7BDE4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2604.16527&json=true","fetch_graph":"https://pith.science/api/pith-number/E7U7BDE4MSF3PXKZTPPI64XW2G/graph.json","fetch_events":"https://pith.science/api/pith-number/E7U7BDE4MSF3PXKZTPPI64XW2G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E7U7BDE4MSF3PXKZTPPI64XW2G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E7U7BDE4MSF3PXKZTPPI64XW2G/action/storage_attestation","attest_author":"https://pith.science/pith/E7U7BDE4MSF3PXKZTPPI64XW2G/action/author_attestation","sign_citation":"https://pith.science/pith/E7U7BDE4MSF3PXKZTPPI64XW2G/action/citation_signature","submit_replication":"https://pith.science/pith/E7U7BDE4MSF3PXKZTPPI64XW2G/action/replication_record"}},"created_at":"2026-06-02T02:04:53.226144+00:00","updated_at":"2026-06-02T02:04:53.226144+00:00"}