{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5FYF6OJDPV77XYPBRZRB7BFTGX","short_pith_number":"pith:5FYF6OJD","schema_version":"1.0","canonical_sha256":"e9705f39237d7ffbe1e18e621f84b335d0a0a33237ea002f3c84c18a9d608134","source":{"kind":"arxiv","id":"2502.13056","version":2},"attestation_state":"computed","paper":{"title":"Benchmarking MedMNIST dataset on real quantum hardware","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Gurinder Singh, Hongni Jin, Kenneth M. Merz Jr","submitted_at":"2025-02-18T17:02:41Z","abstract_excerpt":"Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work, we present the first comprehensive QML study by benchmarking the MedMNIST-a diverse collection of medical imaging datasets on a 127-qubit real IBM quantum hardware, to evaluate the feasibility and performance of quantum models (without any classical neural networks) in practical applications. This study explores recent advancements in quantum computing such as device-aware quantum circuits, error suppression, and mit"},"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":"2502.13056","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2025-02-18T17:02:41Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d8237caff6b3606d86a5990cebdbc1013636bea31aeee4b6e952f7b68827ec5a","abstract_canon_sha256":"49ad89d1bf5df2315395406702a5a5fc59df2eaec78213a57572b8218106540e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:35:46.424091Z","signature_b64":"PVfFL4wd45YAV58vHx84Arz5sttpnqGEnJ0X76YQcG7Ezta9GTqzvdlxNQaFu2CGNkocBmvPYrQPkfWLZLvNCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e9705f39237d7ffbe1e18e621f84b335d0a0a33237ea002f3c84c18a9d608134","last_reissued_at":"2026-07-05T10:35:46.423059Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:35:46.423059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benchmarking MedMNIST dataset on real quantum hardware","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"quant-ph","authors_text":"Gurinder Singh, Hongni Jin, Kenneth M. Merz Jr","submitted_at":"2025-02-18T17:02:41Z","abstract_excerpt":"Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work, we present the first comprehensive QML study by benchmarking the MedMNIST-a diverse collection of medical imaging datasets on a 127-qubit real IBM quantum hardware, to evaluate the feasibility and performance of quantum models (without any classical neural networks) in practical applications. This study explores recent advancements in quantum computing such as device-aware quantum circuits, error suppression, and mit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.13056","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/2502.13056/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":"2502.13056","created_at":"2026-07-05T10:35:46.423177+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.13056v2","created_at":"2026-07-05T10:35:46.423177+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.13056","created_at":"2026-07-05T10:35:46.423177+00:00"},{"alias_kind":"pith_short_12","alias_value":"5FYF6OJDPV77","created_at":"2026-07-05T10:35:46.423177+00:00"},{"alias_kind":"pith_short_16","alias_value":"5FYF6OJDPV77XYPB","created_at":"2026-07-05T10:35:46.423177+00:00"},{"alias_kind":"pith_short_8","alias_value":"5FYF6OJD","created_at":"2026-07-05T10:35:46.423177+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.27411","citing_title":"Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder","ref_index":73,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5FYF6OJDPV77XYPBRZRB7BFTGX","json":"https://pith.science/pith/5FYF6OJDPV77XYPBRZRB7BFTGX.json","graph_json":"https://pith.science/api/pith-number/5FYF6OJDPV77XYPBRZRB7BFTGX/graph.json","events_json":"https://pith.science/api/pith-number/5FYF6OJDPV77XYPBRZRB7BFTGX/events.json","paper":"https://pith.science/paper/5FYF6OJD"},"agent_actions":{"view_html":"https://pith.science/pith/5FYF6OJDPV77XYPBRZRB7BFTGX","download_json":"https://pith.science/pith/5FYF6OJDPV77XYPBRZRB7BFTGX.json","view_paper":"https://pith.science/paper/5FYF6OJD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.13056&json=true","fetch_graph":"https://pith.science/api/pith-number/5FYF6OJDPV77XYPBRZRB7BFTGX/graph.json","fetch_events":"https://pith.science/api/pith-number/5FYF6OJDPV77XYPBRZRB7BFTGX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5FYF6OJDPV77XYPBRZRB7BFTGX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5FYF6OJDPV77XYPBRZRB7BFTGX/action/storage_attestation","attest_author":"https://pith.science/pith/5FYF6OJDPV77XYPBRZRB7BFTGX/action/author_attestation","sign_citation":"https://pith.science/pith/5FYF6OJDPV77XYPBRZRB7BFTGX/action/citation_signature","submit_replication":"https://pith.science/pith/5FYF6OJDPV77XYPBRZRB7BFTGX/action/replication_record"}},"created_at":"2026-07-05T10:35:46.423177+00:00","updated_at":"2026-07-05T10:35:46.423177+00:00"}