{"as_of":"2026-08-15T00:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fbd111f047501e1fda9d65ebbfa17ff219fd17d5bb247bfdf9e5d68995c55ff3","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T00:40:36.002157Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.00019/citation-record","integrity":"/paper/2507.00019/integrity","json":"/paper/2507.00019/citation-record.json","paper":"/paper/2507.00019"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:32.744867Z","title":"Machine learning & artificial intelligence in the quantum domain: a review of recent progress.Reports on Progress in Physics, 81(7):074001, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:32.744867Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:60186909e2aaf35cf63d21b8fcd93adc0c762a32554408a7e95ab1b648e1e814","observation_id":"0fe0bbbd-0578-42d5-a413-0558be43fba3","resolution":{"observed_at":"2026-08-07T00:40:32.744867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:32.825669Z","title":"Quantum machine learning in feature hilbert spaces","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:32.825669Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:b9bc48a7705b4901059fda829bc35157731356cca55ce0191c24c6c064b4d624","observation_id":"807936de-752a-460e-af7b-ee59b292df8c","resolution":{"observed_at":"2026-08-07T00:40:32.825669Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:39.116045Z","title":"Machine learning: Quantum vs classical.IEEE Access, 8:219275–219294, 2020","venue":null,"work_id":"6c00b66e-615f-47c6-83c6-e0c7d17a13be","year":2020},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:32.895467Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:e7725c3966dc480f9cefea5a180aa5843aa1b823f6b91aed552f5a6ed7779b79","observation_id":"aba7608e-5cbc-4a06-8abd-db0a720ef4ee","resolution":{"observed_at":"2026-08-07T00:40:39.149982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:39.002322Z","title":"Sok: quantum computing methods for machine learning optimization.Quan- tum Machine Intelligence, 6(2):47, 2024","venue":null,"work_id":"c2810106-fd29-4c6e-949a-9c674e658d64","year":2024},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:32.983684Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:3b998f182cdeb096c126f4965f9880dc0562701ed39f0a7a95c77fec4a65fbb1","observation_id":"757c17a4-6f5b-4497-888a-589954e74dc5","resolution":{"observed_at":"2026-08-07T00:40:39.048318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:33.070617Z","title":"Parameterized quantum circuits as machine learning models.Quantum science and technology, 4(4):043001, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:33.070617Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:9f6a00e4939465639978b9ecc824ddbf72716835e6ecd6cfdd791c1c7eee1967","observation_id":"1bc5cf12-1af7-4a0b-886b-b0eef07a301a","resolution":{"observed_at":"2026-08-07T00:40:33.070617Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:38.878427Z","title":"Quantum data encoding: A comparative analysis of classical-to- quantum mapping techniques and their impact on machine learning accuracy.EPJ Quantum Technology, 11(1):72, 2024","venue":null,"work_id":"638b69a8-0160-41ff-9c84-adc7320a5804","year":2024},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:33.124189Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:31f37aa28aa1e35a52ec3b40794880230532a403f3dcb1b9f14deb82d6e2ea77","observation_id":"fae7f8bc-1285-4714-bf0c-b38913b1c585","resolution":{"observed_at":"2026-08-07T00:40:38.941970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:38.817906Z","title":"Implementing a distance-based classifier with a quantum interference circuit.Europhysics Letters, 119(6):60002, 2017","venue":null,"work_id":"4253f37e-6486-4f5d-872a-8daad323a46f","year":2017},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:33.204975Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:3a4dcc6736037ad96d4efc4e85c243db3c29c97af04be10f47d48df65dba1a3c","observation_id":"c777e5ad-cfc2-4c5d-a6cf-fd7b4e0fc5ae","resolution":{"observed_at":"2026-08-07T00:40:38.844873Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:38.700121Z","title":"Exponential data encoding for quan- tum supervised learning.Physical Review A, 107(1):012422, 2023","venue":null,"work_id":"c6aaaf39-0388-4454-b09e-0971da1dd339","year":2023},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:33.303781Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:489154bc9e7b2fa80fe27e819a3bde10dbbd91345a5d820fa3f0976444d26d74","observation_id":"543794ba-fe6e-4ed2-8f75-de58a8fd200b","resolution":{"observed_at":"2026-08-07T00:40:38.737197Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:33.427769Z","title":"Robust data encodings for quantum classifiers.Physical Review A, 102(3):032420, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:33.427769Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:3fc5aad4db496261edeb79b7831407ec53397e5a6f926d1c36fe5762cc484d67","observation_id":"859e8e85-0d60-4e25-accc-f6d75fbd6ff7","resolution":{"observed_at":"2026-08-07T00:40:33.427769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:38.575839Z","title":"Quantum computation over continuous variables.Phys- ical Review Letters, 82(8):1784, 1999","venue":null,"work_id":"cc9f2d34-7c8c-4990-9095-6e8ad812a988","year":1999},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:33.543397Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:b1fc41cd791726940e5bf9f9380542e47ce28accb93c0e868bf8a0781af65687","observation_id":"232baecb-ae33-40ca-8f2c-f429c36d9aad","resolution":{"observed_at":"2026-08-07T00:40:38.618778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:38.467787Z","title":"Quantum computing with continuous-variable clusters.Physical Review A—Atomic, Molecu- lar, and Optical Physics, 79(6):062318, 2009","venue":null,"work_id":"ac7deeef-0e49-461c-b63e-8d1600ff0b1c","year":2009},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:33.624091Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:a32a80f204f95f6f37e6796a7dd231955e48b9a9e967afaedf37f1c0e7fb4d1f","observation_id":"c199a4fa-99a1-400c-895b-d91ccb798584","resolution":{"observed_at":"2026-08-07T00:40:38.515587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:38.338087Z","title":"Hybrid discrete-and continuous-variable quantum information.Nature Physics, 11(9):713–719, 2015","venue":null,"work_id":"e69b73d9-4b47-4c2d-aeb7-98d311d10ec7","year":2015},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:33.716095Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:dbcd09c2d464aa05543850cabe1e44433d9214e9dfa470569600ca8225b2493c","observation_id":"9e55f89a-3128-458e-8db0-8c943e11c075","resolution":{"observed_at":"2026-08-07T00:40:38.422778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.07246","last_updated":"2022-06-15T02:20:14Z","snapshot_observed_at":"2026-08-13T15:23:43.230828Z","submitted_at":"2022-06-15T02:20:14Z","title":"Quantum computing overview: discrete vs. continuous variable models","version":1},"cited_work":{"arxiv_id":"2206.07246","doi":null,"metadata_source":"pith","pith_arxiv_id":"2206.07246","snapshot_observed_at":"2026-08-07T00:40:36.099348Z","title":"Quantum computing overview: discrete vs. continuous variable models","venue":"quant-ph","work_id":"48d4a7a0-eadd-42de-85e1-111c00ad5232","year":2022},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:33.824035Z"},"links":{"cited_paper":"/paper/2206.07246","citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:19eefa5d6e5e8c5ca9955d458635ad78649ad21acf68b34d52918397d29124e0","observation_id":"1177b241-ae8c-481c-b205-47c49e2acfae","resolution":{"observed_at":"2026-08-07T00:40:36.137730Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:33.932038Z","title":"Circuit-centric quantum classifiers.Physical Review A, 101(3):032308, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:33.932038Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:6029b3d4222ad8b36eca11a5754312891adfbb02b87e51cefae8f38d819f9cdc","observation_id":"0dbe923a-4c9c-4dc4-a183-691fe97d44c2","resolution":{"observed_at":"2026-08-07T00:40:33.932038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:38.191963Z","title":"Data re- uploading for a universal quantum classifier.Quantum, 4:226, 2020","venue":null,"work_id":"138a4f92-ad0b-4b2c-8c84-8aae6b6be630","year":2020},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:34.036736Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:c5f5eba04bdb3cd8b19e02b6f6cc5459a8f71237618efe28a6a366df95712482","observation_id":"965f121e-dec7-4715-9b05-f93003c3836f","resolution":{"observed_at":"2026-08-07T00:40:38.248798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:38.030984Z","title":"Supervised learning with quantum-enhanced feature spaces.Nature, 567(7747):209–212, 2019","venue":null,"work_id":"b295c0b5-2f0b-4faf-87c1-6c1d33a371e8","year":2019},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:34.181028Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:592b95245b0f948c0807427f2688006251628cc93c6515537da43331c1f5a3a9","observation_id":"2c6545ee-fbc8-499f-9954-c11437eb97ca","resolution":{"observed_at":"2026-08-07T00:40:38.098212Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:34.289924Z","title":"Quantum circuit learning.Physical Review A, 98(3):032309, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:34.289924Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:9219f84f0ad5fe3e7380b45eb4b034f2174a02e710aeb6f42342c57556952072","observation_id":"23e74547-18b8-4ff0-865f-9aa46775e8a2","resolution":{"observed_at":"2026-08-07T00:40:34.289924Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:37.862556Z","title":"The quest for a quantum neural network.Quantum Information Processing, 13(11):2567–2586, 2018","venue":null,"work_id":"ed981e62-a6f7-4835-8572-a0101867855c","year":2018},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:34.396334Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:ea6227c1e545379200e53cafd85dc9f89abb0b778b3448ca68607764a9361293","observation_id":"6d1a45f4-8a65-4255-8f4f-c203e50a3d14","resolution":{"observed_at":"2026-08-07T00:40:37.928760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:37.727030Z","title":"Braunstein and Peter Van Loock","venue":null,"work_id":"6e839542-aba1-4061-bc7e-dd4827d8f7f3","year":2005},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:34.507745Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:56938d27ab33d95936c344bacd620e3482f1655df94bcdaaae1cd6de297ef625","observation_id":"11530475-1642-4b8c-ac5c-3c8de0252925","resolution":{"observed_at":"2026-08-07T00:40:37.784401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:37.587996Z","title":"Quantum embeddings for machine learning.Physical Review A, 101(3):032305, 2020","venue":null,"work_id":"57b145a6-d8aa-4a95-91e6-6aa523391fe8","year":2020},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:34.613618Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:8d5dba86901057a0a46a2b6931c07220e9cd97ca0e80f3e9a86888a4c2b758e2","observation_id":"67adcb83-e259-4031-b869-0deb3185c041","resolution":{"observed_at":"2026-08-07T00:40:37.631990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:37.450568Z","title":"Efficient measurement-based quantum computing with continuous-variable systems.Physical Review A—Atomic, Molecular, and Optical Physics, 85(6):062318, 2012","venue":null,"work_id":"9576fcfb-6a5a-4e6e-bf73-ead810d5f966","year":2012},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:34.727053Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:94d521c7c14ab0569ddc7cf7f0d0d3f963fe4123d823076305d7fdea7f052d31","observation_id":"5abcf107-f37c-414f-9d54-b3107a90e4bf","resolution":{"observed_at":"2026-08-07T00:40:37.518687Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:37.346329Z","title":"Quantum-inspired machine learning: En- coding strategies and interpretability.Quantum, 8:117, 2024","venue":null,"work_id":"f0c30db5-dd83-43df-bc9f-61ad44124d9e","year":2024},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:34.829483Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:4b66e3271ad1c8d0e7b5639d04279a626261c0b7a3cf49c71b69d0c844e27aab","observation_id":"8bc3f622-80d2-46c2-bed7-88682a9d265d","resolution":{"observed_at":"2026-08-07T00:40:37.392374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:37.261390Z","title":"Continuous-variable quantum neural networks.Physical Review Research, 1(3):033063, 2019","venue":null,"work_id":"a9289b9f-45e9-4d45-aa22-dd5708b7ca1e","year":2019},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:34.906640Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:895cb6db014e37cd50e986dd391ea0b2daab7b9553377dc73e45d5bc6c929925","observation_id":"fd4bbdd2-38c7-4524-8444-a0acf5d6ef4b","resolution":{"observed_at":"2026-08-07T00:40:37.313362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:37.193444Z","title":"Measuring analytic gradients of general quantum evolution with the stochastic parameter shift rule.Quantum, 5:386, 2021","venue":null,"work_id":"0e024f65-2cc3-42b1-8aa8-8cc09ae8fc26","year":2021},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:34.977307Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:8e7590ebdb5b303aab5ac4499af60902ad0e5abd737f100c740eedac2c202f59","observation_id":"2b796f26-9f84-420f-9087-9efa3babfbff","resolution":{"observed_at":"2026-08-07T00:40:37.222093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:37.115677Z","title":"Encoding strategies for efficient quantum data representation.Quantum Science and Technology, 8(1):015005, 2023","venue":null,"work_id":"deef41e1-29c2-46fd-8c58-e2aa43342541","year":2023},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:35.056987Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:2fff6f8c442f1e61ac01964249f661524d04a38bd369d3d284f3009a8defd18f","observation_id":"abed4fb7-f7b0-4e5f-96ae-3ade425db538","resolution":{"observed_at":"2026-08-07T00:40:37.164931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:37.070846Z","title":"On fundamental aspects of quantum extreme learning machines","venue":null,"work_id":"10a41e2e-3d41-4a55-a2a3-6ddb69087492","year":2025},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:35.126864Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:4bf499adfa2f7a459f1131d966c10285febc1f600b8834d34cf818b9e4fc8a79","observation_id":"0071b93b-ddaa-4037-8434-2b6b5bd0ee85","resolution":{"observed_at":"2026-08-07T00:40:37.089231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:35.177613Z","title":"A quantum approximate optimization algorithm, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:35.177613Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:b96bf2a223a9c4ace98bdddcccd8eecd5b88845fe1739ac1769196840efaf911","observation_id":"9e868cf0-8fc3-4c99-bbb3-d591605193e6","resolution":{"observed_at":"2026-08-07T00:40:35.177613Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:36.940157Z","title":"Quantum approximate optimization is computationally universal, 2018","venue":null,"work_id":"c7b7c2d2-5459-4608-9fdb-39e0848f5083","year":2018},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:35.283719Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:43e00e63d689258a9af6f8f2530e24d9ac4dacd95fbf30b9577e934834845284","observation_id":"80e44faf-4af1-4998-97d0-bab571afcdd0","resolution":{"observed_at":"2026-08-07T00:40:37.053071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:36.782080Z","title":"Gaussian quantum information.Reviews of Modern Physics, 84(2):621–669, 2012","venue":null,"work_id":"5c2073c3-c73f-4cc3-9a6e-cf74caf03c9b","year":2012},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:35.396023Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:268ffbe6c2bdc45d8c460b816e407b4e51beafb96ee2467bcc78ab5c09c6d98e","observation_id":"583a6aa4-e89b-4288-9d4d-d5fcf3c65037","resolution":{"observed_at":"2026-08-07T00:40:36.831902Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:35.485463Z","title":"John Wiley & Sons, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:35.485463Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:0a408205f5da72423187d6cac76cd95850be47e49667478ac405bba4fef8770e","observation_id":"281e308c-17ee-4e74-8e3a-68c25d7290b9","resolution":{"observed_at":"2026-08-07T00:40:35.485463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:36.626226Z","title":"Nearest neighbor pattern classification.IEEE transactions on information theory, 13(1):21–27, 1967","venue":null,"work_id":"f30f4743-70a8-4e3d-a097-7e093e2d2b11","year":1967},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:35.584651Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:2cc0f7c79099962fa9dc24878b4716f5446bcdbaf2122376d119eb3ad035c764","observation_id":"4d8ea361-aa2f-4ead-89e2-217cbd905f0e","resolution":{"observed_at":"2026-08-07T00:40:36.690373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:36.414245Z","title":"Support-vector networks.Machine learning, 20:273–297, 1995","venue":null,"work_id":"7c1c16c6-1303-41a4-b672-7d3ab3d64738","year":1995},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:35.646640Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:43de5feb460e9bbec0385c5cb300b0137cbeb7c9f001843f971440ccd7fb69cb","observation_id":"10cb0106-3d44-4e36-808a-2a70d6057fda","resolution":{"observed_at":"2026-08-07T00:40:36.498816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:35.745473Z","title":"Random forests.Machine learning, 45:5–32, 2001","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:35.745473Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:a950445eabc5017d0d3f7fb8fc13ab5a195bb5f631e070a8a1ecb5252f7cc30d","observation_id":"0948125c-eb31-45e1-b0ec-f72bbd453fa2","resolution":{"observed_at":"2026-08-07T00:40:35.745473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:35.824569Z","title":"Lightgbm: A highly efficient gradient boosting decision tree.Advances in neural information processing systems, 30, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:35.824569Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:b328c9b31cd88414912d37f1bf3a38576a04c653ab56fff3ce1a818a5990944f","observation_id":"b38a802e-8322-44c3-89b8-e2777d82dda6","resolution":{"observed_at":"2026-08-07T00:40:35.824569Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:35.915882Z","title":"A decision-theoretic generalization of on-line learning and an application to boosting.Journal of computer and system sciences, 55(1):119–139, 1997","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:35.915882Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:d23f34b518e465331bf46fd1f88e89a5b0695958387a3740704a22bc63d08c65","observation_id":"6ac6f402-b99e-4e50-b08e-2297128f9d79","resolution":{"observed_at":"2026-08-07T00:40:35.915882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:40:36.225590Z","title":"Catboost: unbiased boosting with categorical features.Advances in neural information processing systems, 31, 2018","venue":null,"work_id":"d1289848-b6bf-4e72-bdd5-5c3fc8255bcc","year":2018},"citing_paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T00:40:36.002157Z"},"links":{"citing_paper":"/paper/2507.00019"},"observation_digest":"sha256:0d9630734bae2070dbcb29a3b94bb61243cc2f4f6fa0b1f7b9b09bb3c268273e","observation_id":"712f6837-04f9-4e6a-8aba-7839837c5e9e","resolution":{"observed_at":"2026-08-07T00:40:36.289412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.00019","last_updated":"2025-06-15T13:50:57Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T00:34:08.926462Z","submitted_at":"2025-06-15T13:50:57Z","title":"Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":1,"verified_fuzzy":24},"total_outbound_references":36},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.00019."}