{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:NTHALDRDR32LESVDASE5UUM7BR","short_pith_number":"pith:NTHALDRD","schema_version":"1.0","canonical_sha256":"6cce058e238ef4b24aa30489da519f0c5536fe7ed1f0f0ead8dd9828211cfd49","source":{"kind":"arxiv","id":"2108.10793","version":3},"attestation_state":"computed","paper":{"title":"Bosonic field digitization for quantum computers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.str-el","hep-ph"],"primary_cat":"quant-ph","authors_text":"Alexandru Macridin, Andy C. Y. Li, Panagiotis Spentzouris, Stephen Mrenna","submitted_at":"2021-08-24T15:30:04Z","abstract_excerpt":"Quantum simulation of quantum field theory is a flagship application of quantum computers that promises to deliver capabilities beyond classical computing. The realization of quantum advantage will require methods to accurately predict error scaling as a function of the resolution and parameters of the model that can be implemented efficiently on quantum hardware. In this paper, we address the representation of lattice bosonic fields in a discretized field amplitude basis, develop methods to predict error scaling, and present efficient qubit implementation strategies. A low-energy subspace of "},"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":"2108.10793","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2021-08-24T15:30:04Z","cross_cats_sorted":["cond-mat.str-el","hep-ph"],"title_canon_sha256":"6268e3f778c986c48551e35ff5b3ff84bb792b9419d685c973d41ec32e4d317b","abstract_canon_sha256":"798ad53e2c98ee87335caed686eb5c63e7acc5ff6914acde767075ae297e03a2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:20:15.711283Z","signature_b64":"8h/fBz/Rfpuo/HzSFkIfN95ZeJTaa6kP2VFAUGVq9xnHCmHr0kEInoMsmBJkb/27KebMAWFbYOOJgybUQJbaDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6cce058e238ef4b24aa30489da519f0c5536fe7ed1f0f0ead8dd9828211cfd49","last_reissued_at":"2026-07-05T04:20:15.710787Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:20:15.710787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bosonic field digitization for quantum computers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.str-el","hep-ph"],"primary_cat":"quant-ph","authors_text":"Alexandru Macridin, Andy C. Y. Li, Panagiotis Spentzouris, Stephen Mrenna","submitted_at":"2021-08-24T15:30:04Z","abstract_excerpt":"Quantum simulation of quantum field theory is a flagship application of quantum computers that promises to deliver capabilities beyond classical computing. The realization of quantum advantage will require methods to accurately predict error scaling as a function of the resolution and parameters of the model that can be implemented efficiently on quantum hardware. In this paper, we address the representation of lattice bosonic fields in a discretized field amplitude basis, develop methods to predict error scaling, and present efficient qubit implementation strategies. A low-energy subspace of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.10793","kind":"arxiv","version":3},"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/2108.10793/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":"2108.10793","created_at":"2026-07-05T04:20:15.710847+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.10793v3","created_at":"2026-07-05T04:20:15.710847+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.10793","created_at":"2026-07-05T04:20:15.710847+00:00"},{"alias_kind":"pith_short_12","alias_value":"NTHALDRDR32L","created_at":"2026-07-05T04:20:15.710847+00:00"},{"alias_kind":"pith_short_16","alias_value":"NTHALDRDR32LESVD","created_at":"2026-07-05T04:20:15.710847+00:00"},{"alias_kind":"pith_short_8","alias_value":"NTHALDRD","created_at":"2026-07-05T04:20:15.710847+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.24896","citing_title":"Tightening energy-based boson truncation bound using Monte Carlo-assisted methods","ref_index":158,"is_internal_anchor":false},{"citing_arxiv_id":"2604.24896","citing_title":"Tightening energy-based boson truncation bound using Monte Carlo-assisted methods","ref_index":158,"is_internal_anchor":false},{"citing_arxiv_id":"2604.24896","citing_title":"Tightening energy-based boson truncation bound using Monte Carlo-assisted methods","ref_index":158,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NTHALDRDR32LESVDASE5UUM7BR","json":"https://pith.science/pith/NTHALDRDR32LESVDASE5UUM7BR.json","graph_json":"https://pith.science/api/pith-number/NTHALDRDR32LESVDASE5UUM7BR/graph.json","events_json":"https://pith.science/api/pith-number/NTHALDRDR32LESVDASE5UUM7BR/events.json","paper":"https://pith.science/paper/NTHALDRD"},"agent_actions":{"view_html":"https://pith.science/pith/NTHALDRDR32LESVDASE5UUM7BR","download_json":"https://pith.science/pith/NTHALDRDR32LESVDASE5UUM7BR.json","view_paper":"https://pith.science/paper/NTHALDRD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.10793&json=true","fetch_graph":"https://pith.science/api/pith-number/NTHALDRDR32LESVDASE5UUM7BR/graph.json","fetch_events":"https://pith.science/api/pith-number/NTHALDRDR32LESVDASE5UUM7BR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NTHALDRDR32LESVDASE5UUM7BR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NTHALDRDR32LESVDASE5UUM7BR/action/storage_attestation","attest_author":"https://pith.science/pith/NTHALDRDR32LESVDASE5UUM7BR/action/author_attestation","sign_citation":"https://pith.science/pith/NTHALDRDR32LESVDASE5UUM7BR/action/citation_signature","submit_replication":"https://pith.science/pith/NTHALDRDR32LESVDASE5UUM7BR/action/replication_record"}},"created_at":"2026-07-05T04:20:15.710847+00:00","updated_at":"2026-07-05T04:20:15.710847+00:00"}