{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:SFEGJMKCOTZ22IC3FCV5D4S7KC","short_pith_number":"pith:SFEGJMKC","schema_version":"1.0","canonical_sha256":"914864b14274f3ad205b28abd1f25f509b683110763137885355e8831bf988a9","source":{"kind":"arxiv","id":"2311.01120","version":2},"attestation_state":"computed","paper":{"title":"EHA: Entanglement-variational Hardware-efficient Ansatz for Eigensolvers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Bo Qi, Daoyi Dong, Xin Wang, Yabo Wang","submitted_at":"2023-11-02T09:58:02Z","abstract_excerpt":"Variational quantum eigensolvers (VQEs) are one of the most important and effective applications of quantum computing, especially in the current noisy intermediate-scale quantum (NISQ) era. There are mainly two ways for VQEs: problem-agnostic and problem-specific. For problem-agnostic methods, they often suffer from trainability issues. For problem-specific methods, their performance usually relies upon choices of initial reference states which are often hard to determine. In this paper, we propose an Entanglement-variational Hardware-efficient Ansatz (EHA), and numerically compare it with som"},"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":"2311.01120","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2023-11-02T09:58:02Z","cross_cats_sorted":[],"title_canon_sha256":"e08ae98f3b00614ff9aed03b81d0e19d2ec95ac79b0901cb81e2091deb8f54c2","abstract_canon_sha256":"ec7d03d97049c9e1f3a1fffd660d4a29d6a3753aef8fbfac1cac761e051da3df"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:08.177091Z","signature_b64":"Cn6Q7GIRtQEozBB6Uk4Rvqy6FwEFKt9pw0iOzZdIaDpXnXOkgt6jLOaLQ4Vfq5McxIvV5qMmiaMC0cdOO12uAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"914864b14274f3ad205b28abd1f25f509b683110763137885355e8831bf988a9","last_reissued_at":"2026-07-05T09:36:08.176613Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:08.176613Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EHA: Entanglement-variational Hardware-efficient Ansatz for Eigensolvers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Bo Qi, Daoyi Dong, Xin Wang, Yabo Wang","submitted_at":"2023-11-02T09:58:02Z","abstract_excerpt":"Variational quantum eigensolvers (VQEs) are one of the most important and effective applications of quantum computing, especially in the current noisy intermediate-scale quantum (NISQ) era. There are mainly two ways for VQEs: problem-agnostic and problem-specific. For problem-agnostic methods, they often suffer from trainability issues. For problem-specific methods, their performance usually relies upon choices of initial reference states which are often hard to determine. In this paper, we propose an Entanglement-variational Hardware-efficient Ansatz (EHA), and numerically compare it with som"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.01120","kind":"arxiv","version":2},"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/2311.01120/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":"2311.01120","created_at":"2026-07-05T09:36:08.176672+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.01120v2","created_at":"2026-07-05T09:36:08.176672+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.01120","created_at":"2026-07-05T09:36:08.176672+00:00"},{"alias_kind":"pith_short_12","alias_value":"SFEGJMKCOTZ2","created_at":"2026-07-05T09:36:08.176672+00:00"},{"alias_kind":"pith_short_16","alias_value":"SFEGJMKCOTZ22IC3","created_at":"2026-07-05T09:36:08.176672+00:00"},{"alias_kind":"pith_short_8","alias_value":"SFEGJMKC","created_at":"2026-07-05T09:36:08.176672+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.01183","citing_title":"Neural Network-Based Frequency Optimization for Superconducting Quantum Chips","ref_index":33,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SFEGJMKCOTZ22IC3FCV5D4S7KC","json":"https://pith.science/pith/SFEGJMKCOTZ22IC3FCV5D4S7KC.json","graph_json":"https://pith.science/api/pith-number/SFEGJMKCOTZ22IC3FCV5D4S7KC/graph.json","events_json":"https://pith.science/api/pith-number/SFEGJMKCOTZ22IC3FCV5D4S7KC/events.json","paper":"https://pith.science/paper/SFEGJMKC"},"agent_actions":{"view_html":"https://pith.science/pith/SFEGJMKCOTZ22IC3FCV5D4S7KC","download_json":"https://pith.science/pith/SFEGJMKCOTZ22IC3FCV5D4S7KC.json","view_paper":"https://pith.science/paper/SFEGJMKC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.01120&json=true","fetch_graph":"https://pith.science/api/pith-number/SFEGJMKCOTZ22IC3FCV5D4S7KC/graph.json","fetch_events":"https://pith.science/api/pith-number/SFEGJMKCOTZ22IC3FCV5D4S7KC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SFEGJMKCOTZ22IC3FCV5D4S7KC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SFEGJMKCOTZ22IC3FCV5D4S7KC/action/storage_attestation","attest_author":"https://pith.science/pith/SFEGJMKCOTZ22IC3FCV5D4S7KC/action/author_attestation","sign_citation":"https://pith.science/pith/SFEGJMKCOTZ22IC3FCV5D4S7KC/action/citation_signature","submit_replication":"https://pith.science/pith/SFEGJMKCOTZ22IC3FCV5D4S7KC/action/replication_record"}},"created_at":"2026-07-05T09:36:08.176672+00:00","updated_at":"2026-07-05T09:36:08.176672+00:00"}