{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:PYRJ6LYVFUAOX2OSENWH3REPOD","short_pith_number":"pith:PYRJ6LYV","schema_version":"1.0","canonical_sha256":"7e229f2f152d00ebe9d2236c7dc48f70deac12428e56bc3e65d55f170539aed6","source":{"kind":"arxiv","id":"2605.23138","version":1},"attestation_state":"computed","paper":{"title":"Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.LG"],"primary_cat":"quant-ph","authors_text":"Dhanvi Bharadwaj, Gino Kwun, Gokul Subramanian Ravi","submitted_at":"2026-05-22T01:24:54Z","abstract_excerpt":"Variational Quantum Algorithms (VQAs) potentially offer a pathway to practical quantum advantage, but their optimization is heavily hindered by barren plateaus and numerous local minima. While classically simulable Clifford circuits can warm-start VQAs to accelerate convergence, existing heuristic-based initialization methods struggle to scale within vast combinatorial search spaces. To overcome this bottleneck, we propose CRiSP (a Clifford Reinforcement Learning agent for State Preparation), a framework that formulates discrete prefix selection as a sequential decision-making problem. CRiSP u"},"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":"2605.23138","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-22T01:24:54Z","cross_cats_sorted":["cs.AI","cs.ET","cs.LG"],"title_canon_sha256":"b438562243f1c641c14b08ab10a5f53a93d9788450bf295b27b91293e287c90f","abstract_canon_sha256":"e29cff70764361cea0c05dce0231103f5af962c64edd5e04ef3aaf800a0e797c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:01:40.016987Z","signature_b64":"FGLN8BG7vrF3Ab4BkObPo4pTtLA9AtoHmU6xJASmbSEvR4YX1vDWzrCFszG/LATQ1dLyfdUamu64tspVr9eWDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e229f2f152d00ebe9d2236c7dc48f70deac12428e56bc3e65d55f170539aed6","last_reissued_at":"2026-05-25T02:01:40.016273Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:01:40.016273Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.LG"],"primary_cat":"quant-ph","authors_text":"Dhanvi Bharadwaj, Gino Kwun, Gokul Subramanian Ravi","submitted_at":"2026-05-22T01:24:54Z","abstract_excerpt":"Variational Quantum Algorithms (VQAs) potentially offer a pathway to practical quantum advantage, but their optimization is heavily hindered by barren plateaus and numerous local minima. While classically simulable Clifford circuits can warm-start VQAs to accelerate convergence, existing heuristic-based initialization methods struggle to scale within vast combinatorial search spaces. To overcome this bottleneck, we propose CRiSP (a Clifford Reinforcement Learning agent for State Preparation), a framework that formulates discrete prefix selection as a sequential decision-making problem. CRiSP u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.23138","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/2605.23138/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":"2605.23138","created_at":"2026-05-25T02:01:40.016403+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.23138v1","created_at":"2026-05-25T02:01:40.016403+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.23138","created_at":"2026-05-25T02:01:40.016403+00:00"},{"alias_kind":"pith_short_12","alias_value":"PYRJ6LYVFUAO","created_at":"2026-05-25T02:01:40.016403+00:00"},{"alias_kind":"pith_short_16","alias_value":"PYRJ6LYVFUAOX2OS","created_at":"2026-05-25T02:01:40.016403+00:00"},{"alias_kind":"pith_short_8","alias_value":"PYRJ6LYV","created_at":"2026-05-25T02:01:40.016403+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/PYRJ6LYVFUAOX2OSENWH3REPOD","json":"https://pith.science/pith/PYRJ6LYVFUAOX2OSENWH3REPOD.json","graph_json":"https://pith.science/api/pith-number/PYRJ6LYVFUAOX2OSENWH3REPOD/graph.json","events_json":"https://pith.science/api/pith-number/PYRJ6LYVFUAOX2OSENWH3REPOD/events.json","paper":"https://pith.science/paper/PYRJ6LYV"},"agent_actions":{"view_html":"https://pith.science/pith/PYRJ6LYVFUAOX2OSENWH3REPOD","download_json":"https://pith.science/pith/PYRJ6LYVFUAOX2OSENWH3REPOD.json","view_paper":"https://pith.science/paper/PYRJ6LYV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.23138&json=true","fetch_graph":"https://pith.science/api/pith-number/PYRJ6LYVFUAOX2OSENWH3REPOD/graph.json","fetch_events":"https://pith.science/api/pith-number/PYRJ6LYVFUAOX2OSENWH3REPOD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PYRJ6LYVFUAOX2OSENWH3REPOD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PYRJ6LYVFUAOX2OSENWH3REPOD/action/storage_attestation","attest_author":"https://pith.science/pith/PYRJ6LYVFUAOX2OSENWH3REPOD/action/author_attestation","sign_citation":"https://pith.science/pith/PYRJ6LYVFUAOX2OSENWH3REPOD/action/citation_signature","submit_replication":"https://pith.science/pith/PYRJ6LYVFUAOX2OSENWH3REPOD/action/replication_record"}},"created_at":"2026-05-25T02:01:40.016403+00:00","updated_at":"2026-05-25T02:01:40.016403+00:00"}