{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:ACZ3BT5W7AWSESHA6CCY5EQ5GC","short_pith_number":"pith:ACZ3BT5W","schema_version":"1.0","canonical_sha256":"00b3b0cfb6f82d2248e0f0858e921d3094a0da41dea88b003cacb44e018113df","source":{"kind":"arxiv","id":"2104.03295","version":1},"attestation_state":"computed","paper":{"title":"Experimental Quantum Learning of a Spectral Decomposition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Andrew Sornborger, Michael R. Geller, Patrick J. Coles, Zo\\\"e Holmes","submitted_at":"2021-04-07T17:53:50Z","abstract_excerpt":"Currently available quantum hardware allows for small scale implementations of quantum machine learning algorithms. Such experiments aid the search for applications of quantum computers by benchmarking the near-term feasibility of candidate algorithms. Here we demonstrate the quantum learning of a two-qubit unitary by a sequence of three parameterized quantum circuits containing a total of 21 variational parameters. Moreover, we variationally diagonalize the unitary to learn its spectral decomposition, i.e., its eigenvalues and eigenvectors. We illustrate how this can be used as a subroutine t"},"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":"2104.03295","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2021-04-07T17:53:50Z","cross_cats_sorted":[],"title_canon_sha256":"926e1d5c6b4a04b87a6f02a11d1cf7d3c7c9700ce5cb144615e6a40fa2b25292","abstract_canon_sha256":"2683c769ab44cb73b89c3fa1da2dbed727e2ef1add2db401e93d1fbb5c1f916e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:12:02.532829Z","signature_b64":"GoSsMq9QU8tJTxU1VTTFrLG6vfSCZNXiEO0LLpUPzgPxhegP8xDv19g5IJaU8+qt479FCP3/hcWOdKncDDURCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"00b3b0cfb6f82d2248e0f0858e921d3094a0da41dea88b003cacb44e018113df","last_reissued_at":"2026-07-05T03:12:02.532426Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:12:02.532426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Experimental Quantum Learning of a Spectral Decomposition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Andrew Sornborger, Michael R. Geller, Patrick J. Coles, Zo\\\"e Holmes","submitted_at":"2021-04-07T17:53:50Z","abstract_excerpt":"Currently available quantum hardware allows for small scale implementations of quantum machine learning algorithms. Such experiments aid the search for applications of quantum computers by benchmarking the near-term feasibility of candidate algorithms. Here we demonstrate the quantum learning of a two-qubit unitary by a sequence of three parameterized quantum circuits containing a total of 21 variational parameters. Moreover, we variationally diagonalize the unitary to learn its spectral decomposition, i.e., its eigenvalues and eigenvectors. We illustrate how this can be used as a subroutine t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.03295","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/2104.03295/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":"2104.03295","created_at":"2026-07-05T03:12:02.532479+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.03295v1","created_at":"2026-07-05T03:12:02.532479+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.03295","created_at":"2026-07-05T03:12:02.532479+00:00"},{"alias_kind":"pith_short_12","alias_value":"ACZ3BT5W7AWS","created_at":"2026-07-05T03:12:02.532479+00:00"},{"alias_kind":"pith_short_16","alias_value":"ACZ3BT5W7AWSESHA","created_at":"2026-07-05T03:12:02.532479+00:00"},{"alias_kind":"pith_short_8","alias_value":"ACZ3BT5W","created_at":"2026-07-05T03:12:02.532479+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/ACZ3BT5W7AWSESHA6CCY5EQ5GC","json":"https://pith.science/pith/ACZ3BT5W7AWSESHA6CCY5EQ5GC.json","graph_json":"https://pith.science/api/pith-number/ACZ3BT5W7AWSESHA6CCY5EQ5GC/graph.json","events_json":"https://pith.science/api/pith-number/ACZ3BT5W7AWSESHA6CCY5EQ5GC/events.json","paper":"https://pith.science/paper/ACZ3BT5W"},"agent_actions":{"view_html":"https://pith.science/pith/ACZ3BT5W7AWSESHA6CCY5EQ5GC","download_json":"https://pith.science/pith/ACZ3BT5W7AWSESHA6CCY5EQ5GC.json","view_paper":"https://pith.science/paper/ACZ3BT5W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.03295&json=true","fetch_graph":"https://pith.science/api/pith-number/ACZ3BT5W7AWSESHA6CCY5EQ5GC/graph.json","fetch_events":"https://pith.science/api/pith-number/ACZ3BT5W7AWSESHA6CCY5EQ5GC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ACZ3BT5W7AWSESHA6CCY5EQ5GC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ACZ3BT5W7AWSESHA6CCY5EQ5GC/action/storage_attestation","attest_author":"https://pith.science/pith/ACZ3BT5W7AWSESHA6CCY5EQ5GC/action/author_attestation","sign_citation":"https://pith.science/pith/ACZ3BT5W7AWSESHA6CCY5EQ5GC/action/citation_signature","submit_replication":"https://pith.science/pith/ACZ3BT5W7AWSESHA6CCY5EQ5GC/action/replication_record"}},"created_at":"2026-07-05T03:12:02.532479+00:00","updated_at":"2026-07-05T03:12:02.532479+00:00"}