{"as_of":"2026-08-13T23:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c7dae6f821d4bbeb92a920808e48d13c72ad4aa3f86ab98f5183f10e16e559e6","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T15:51:29.735938Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-25T07:38:56.051810Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-25T07:40:28.827763Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"cited_work":{"arxiv_id":"2508.19381","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.19381","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Towards quantum machine learning for malicious code analysis","venue":null,"work_id":"badecb25-d4c8-4691-972b-da8881ab8184","year":2025},"citing_paper":{"arxiv_id":"2511.14989","last_updated":"2026-05-21T23:18:26Z","snapshot_observed_at":"2026-08-02T23:14:07.717727Z","submitted_at":"2025-11-19T00:13:17Z","title":"SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-21T18:42:35.602685Z"},"links":{"cited_paper":"/paper/2508.19381","citing_paper":"/paper/2511.14989"},"observation_digest":"sha256:1cabdee7b11a9bebca9cc3845b05f8bff86d0bf8f63a14fc5088f47b973b7a35","observation_id":"8335b7f2-77db-4a47-a92c-66015cbb8938","resolution":{"observed_at":"2026-05-21T18:44:18.925004Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"cited_work":{"arxiv_id":"2508.19381","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.19381","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Towards quantum machine learning for malicious code analysis","venue":null,"work_id":"badecb25-d4c8-4691-972b-da8881ab8184","year":2025},"citing_paper":{"arxiv_id":"2511.14989","last_updated":"2026-05-21T23:18:26Z","snapshot_observed_at":"2026-08-02T23:14:07.717727Z","submitted_at":"2025-11-19T00:13:17Z","title":"SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness","version":4},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-25T07:38:56.051810Z"},"links":{"cited_paper":"/paper/2508.19381","citing_paper":"/paper/2511.14989"},"observation_digest":"sha256:baec48ed53eb3c425c417290bfc3e1b606212ffd38cfe8e3419eae448a02b3eb","observation_id":"3bd5184d-4c7b-43d2-8e3d-beb93bddca36","resolution":{"observed_at":"2026-05-25T07:40:28.830967Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2508.19381/citation-record","integrity":"/paper/2508.19381/integrity","json":"/paper/2508.19381/citation-record.json","paper":"/paper/2508.19381"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T15:51:30.179347Z","title":"Diffusion-inspired quantum noise mitigation in parameterized quantum circuits,","venue":null,"work_id":"457639bc-7d01-4c56-8390-ca10dd2fee1f","year":2025},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.638158Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:4ed6c838c83db9547c8aa959e2db3a4d1c4530e4bcb711da07fca78d72de5214","observation_id":"7c1b955c-1321-4bee-9d52-907a90db16e8","resolution":{"observed_at":"2026-08-05T15:51:30.182532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.170721Z","title":"Challenges and opportunities in quantum machine learning,","venue":null,"work_id":"26171f6f-7ce5-48c1-aa65-23e16d747c4c","year":2022},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.641818Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:a729e031cfa00ba7f53f5ff894c1d02e889ac19024df66affba620c852824be4","observation_id":"f42c20d0-c8d1-47df-b14c-41b10ffcf653","resolution":{"observed_at":"2026-08-05T15:51:30.173798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.161790Z","title":"Quantum machine learning,","venue":null,"work_id":"73177edc-9674-4608-ba25-f554a45c52e1","year":2017},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.645111Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:26a0bf7dcad3204158a4d5622c15bdde6e33969705bda48a6c9d6bcce39b0e96","observation_id":"187a6a81-5140-4a23-815b-2fb5cb3b655b","resolution":{"observed_at":"2026-08-05T15:51:30.164835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.153183Z","title":"The quest for a quantum neural network,","venue":null,"work_id":"479bf2a7-330e-4ed5-95b8-82ee563cd9d8","year":2014},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.648229Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:f33496e54c014c264ee85f47978cc456a7343f3ce694757cfe3d2fa98ac37abb","observation_id":"a2069ea5-27d1-4ef6-a3d7-9805c87fa783","resolution":{"observed_at":"2026-08-05T15:51:30.156254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.144457Z","title":"The power of quantum neural networks,","venue":null,"work_id":"1051bb69-b3a1-484c-b221-98f14286946b","year":2021},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.651657Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:62f359933b866b10e5dbc0bd6cf02d48ef8813214ec4e36563fbc75eecf5e07d","observation_id":"c00942d6-3dfe-4456-a857-7e842e9c877a","resolution":{"observed_at":"2026-08-05T15:51:30.147616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.135459Z","title":"Quantum computing in the nisq era and beyond,","venue":null,"work_id":"2fd6c992-9983-4e36-a8a7-fc3a712d3e78","year":2018},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.654752Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:7663def18e36758d8c4799499760887051615c1559debbe326c1091063766b0d","observation_id":"1098f2bf-8e58-433a-878d-2bdfe5e86730","resolution":{"observed_at":"2026-08-05T15:51:30.138599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.00661","last_updated":"2022-02-11T10:34:22Z","snapshot_observed_at":"2026-08-13T18:35:31.364752Z","submitted_at":"2021-08-02T06:48:34Z","title":"Quantum convolutional neural network for classical data classification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.00661","snapshot_observed_at":"2026-08-05T15:51:29.658117Z","title":"Quantum convolutional neural network for classical data classification,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.658117Z"},"links":{"cited_paper":"/paper/2108.00661","citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:4afc6d0cebdc9f6d8f24dbab83f20e8abf48f00a938a79e8f6b951369885a1d7","observation_id":"21ce99be-7718-4553-829f-81df16d41fba","resolution":{"observed_at":"2026-08-05T15:51:29.658117Z","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":"2026-08-05T15:51:30.126527Z","title":"QuantumNAS: Noise-adaptive search for robust quantum circuits,","venue":null,"work_id":"ebd4f816-dceb-44c9-ab82-1031b71d46ce","year":2022},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.661446Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:4569c7deee8c5bcf0c9641f48ce595f5803568afd093ada81010fd0678455b48","observation_id":"d1530eb4-6758-49fa-8a35-2cccb08e8f74","resolution":{"observed_at":"2026-08-05T15:51:30.129639Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.11331","last_updated":"2025-01-27T20:12:32Z","snapshot_observed_at":"2026-08-13T17:48:37.044833Z","submitted_at":"2021-10-21T17:59:19Z","title":"QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization","version":5},"cited_work":{"arxiv_id":"2110.11331","doi":null,"metadata_source":"pith","pith_arxiv_id":"2110.11331","snapshot_observed_at":"2026-08-05T15:51:29.937329Z","title":"QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization","venue":"cs.LG","work_id":"f5cdc2be-965a-4998-94a7-91cb59ec84da","year":2021},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.664482Z"},"links":{"cited_paper":"/paper/2110.11331","citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:347f64869f16cac7ecfa097ecb3f784ec3fcb3a29fbc26dd42e237096ba48966","observation_id":"1adc78f6-53a8-41f2-9542-ec26c62a0654","resolution":{"observed_at":"2026-08-05T15:51:29.941120Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.117641Z","title":"An evaluation of hardware-efficient quantum neural networks,","venue":null,"work_id":"8747ecec-5499-4337-bd4b-d532311e3cfe","year":2022},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.667708Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:37d6ee14e4fc3c3b4ff93604c1a30a7c62200faadb3ea0ef40880e5a63f3d04d","observation_id":"06be2372-1559-41ff-8f8a-f9cb93344ab0","resolution":{"observed_at":"2026-08-05T15:51:30.120968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.04968","last_updated":"2022-07-29T22:39:54Z","snapshot_observed_at":"2026-07-06T07:14:13.912107Z","submitted_at":"2018-11-12T19:18:57Z","title":"PennyLane: Automatic differentiation of hybrid quantum-classical computations","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1811.04968","snapshot_observed_at":"2026-08-05T15:51:29.670648Z","title":"Penny- Lane: Automatic differentiation of hybrid quantum-classical computa- tions,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.670648Z"},"links":{"cited_paper":"/paper/1811.04968","citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:887d4de6b47ca4cbf1503ed549369f6c0f6ddd352f19cc4e9d9896e421f98f98","observation_id":"145a56f4-59a7-48af-b212-827258d3808f","resolution":{"observed_at":"2026-08-05T15:51:29.670648Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.02989","last_updated":"2021-08-26T18:00:02Z","snapshot_observed_at":"2026-08-13T15:13:22.172971Z","submitted_at":"2020-03-06T01:31:43Z","title":"TensorFlow Quantum: A Software Framework for Quantum Machine Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.02989","snapshot_observed_at":"2026-08-05T15:51:29.674099Z","title":"TensorFlow Quantum: A software framework for quantum machine learning,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.674099Z"},"links":{"cited_paper":"/paper/2003.02989","citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:b4de7081afcfea3c9460c932942be016096f630245d5caaa7bfede9c5c60d77b","observation_id":"f82a984c-8693-4b07-870b-b9ad5e850651","resolution":{"observed_at":"2026-08-05T15:51:29.674099Z","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":"2026-08-05T15:51:30.108574Z","title":"QMLP: An error-tolerant nonlinear quantum mlp architecture using parameterized two-qubit gates,","venue":null,"work_id":"513c6c9e-8355-4006-b295-de325121ce31","year":2022},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.677552Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:bdc7ccf4b6a2a3bc4eed4cdeb80eaaaec8cf736c4abbab705aafcfe74ce6a5c5","observation_id":"3b2e6bb0-7e0e-41dc-8471-f9d7a4df8714","resolution":{"observed_at":"2026-08-05T15:51:30.112048Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.099862Z","title":"Quantum machine learning for chemistry and physics,","venue":null,"work_id":"785f4557-f074-448e-90cd-eb9229bf6f31","year":2022},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.680432Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:29fa592321d34ac0d3e66d3df282f83be9827bafa1443d1e1ee40952354e5d24","observation_id":"e7f21e88-eeae-46f8-86fe-4778e9afd3d6","resolution":{"observed_at":"2026-08-05T15:51:30.102981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.090815Z","title":"Accelerated discovery of efficient solar cell materials using quantum and machine-learning methods,","venue":null,"work_id":"f635a641-5ef1-4e6b-94b9-b71c31fce484","year":2019},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.683245Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:5af53dd099b5216c1ddfedfb37205a889554d74dde4c3ecd213a471c4786cd89","observation_id":"251353e9-3fe0-4341-bac3-abd095502cea","resolution":{"observed_at":"2026-08-05T15:51:30.094069Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.081970Z","title":"Quantum convolutional neural networks,","venue":null,"work_id":"029cf019-738b-47ef-88a4-30c93a3bd47c","year":2019},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.686122Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:4048b522592640b93f212217dd2fc3138c8e900db23ce21fea7fb9b547ba3acb","observation_id":"1343e435-6a82-4252-9729-bb7e5509ae4c","resolution":{"observed_at":"2026-08-05T15:51:30.085240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.04637","last_updated":"2018-04-16T20:43:33Z","snapshot_observed_at":"2026-07-06T06:33:11.138971Z","submitted_at":"2018-04-12T17:23:56Z","title":"EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.04637","snapshot_observed_at":"2026-08-05T15:51:29.689009Z","title":"EMBER: An open dataset for training static PE malware machine learning models,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.689009Z"},"links":{"cited_paper":"/paper/1804.04637","citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:ccf78788e2f26588fe6ea59d7c8e28f78444ffd65cce19b243e5f9529282e069","observation_id":"8cfb7877-e26a-4203-a3b8-173033eac3d1","resolution":{"observed_at":"2026-08-05T15:51:29.689009Z","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":"2026-08-05T15:51:30.073036Z","title":"Enhancing state-of-the-art classifiers with api semantics to detect evolved android malware,","venue":null,"work_id":"ee1c5d01-fa6c-4e53-b070-7afa699782d9","year":2020},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.692270Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:93511f4e69f9757493b7dcbb1f1c87b987818f79036784ee42542c4742dc8180","observation_id":"9fe65e27-5076-4aee-b9af-f5f3c0aa4f80","resolution":{"observed_at":"2026-08-05T15:51:30.076270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.063820Z","title":"Drebin: Effective and explainable detection of android malware in your pocket,","venue":null,"work_id":"0b7538b2-0231-404b-a862-983a9147cf22","year":2014},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.695129Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:002a157269d5e26d22e3354260673ddb99df442ebb969651e682164f7c691571","observation_id":"b999f492-8349-4d11-b92c-40cc4072c3ea","resolution":{"observed_at":"2026-08-05T15:51:30.067049Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.054599Z","title":"On the limitations of continual learning for malware classification,","venue":null,"work_id":"f453fedb-3dcf-4315-aa57-55d8937c50d1","year":2022},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.698027Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:7310586c0b13e53bc36d0a3073268e87a11f6392bb57d32afef78437cfe3ab37","observation_id":"b136112a-2bff-4a0d-ab21-5b08b9972095","resolution":{"observed_at":"2026-08-05T15:51:30.058170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.045070Z","title":"MalCL: Lever- aging gan-based generative replay to combat catastrophic forgetting in malware classification,","venue":null,"work_id":"55ac6318-c506-4fd7-a1f1-42ea0b38c5b2","year":2025},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.701063Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:476c6999587f7390f412981ed4a543cce9aa160defeca80990d5d84da07a48ac","observation_id":"04ed17d1-f311-4b85-8a51-c9ae5505e9b8","resolution":{"observed_at":"2026-08-05T15:51:30.048777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2502.05760","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T15:51:29.896689Z","title":"MADAR: Efficient continual learning for malware analysis with diversity-aware replay,","venue":null,"work_id":"2fc1a218-b647-456a-86c3-8110d7224b92","year":2025},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.703922Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:0989787769a57bdc01c2d7a4539c0ac4587b5fcd98c49a392a23d21c271200e9","observation_id":"ac7c548d-ffef-4923-a54e-32a9634632db","resolution":{"observed_at":"2026-08-05T15:51:29.902675Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.035810Z","title":"A hybrid quantum-classical neural network architecture for binary classification,","venue":null,"work_id":"0ca04c9b-5713-4740-923b-ce30aab0822a","year":2022},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.706836Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:6f7e204155a930d6476bf90dd3b2d2167cd3550ca72da07995c61f6b3a34cb85","observation_id":"9a37bf57-bb27-48d9-a5dd-78a3ff7bec0d","resolution":{"observed_at":"2026-08-05T15:51:30.039142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.025675Z","title":"Benchmarking adversarially robust quantum machine learning at scale,","venue":null,"work_id":"8536cdc7-d01d-4d36-a03a-35945f48f9a8","year":2023},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.709617Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:ce3fb0bb95dadd05aa513c0cb2e4d589b5277412a9f7b2a08c535b3479dca6a6","observation_id":"76a107db-3954-4754-9626-acc08f6fbb51","resolution":{"observed_at":"2026-08-05T15:51:30.029551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.014961Z","title":"Qucnn : A quantum convolu- tional neural network with entanglement based backpropagation,","venue":null,"work_id":"7264483a-d087-4659-a0a0-56642da63a24","year":2022},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.712462Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:a2f05179c65bd8fe942ce628fce2bdf45f994185416426d00559b6f36c7f1047","observation_id":"8b8e9273-5463-492b-b32d-522794897c05","resolution":{"observed_at":"2026-08-05T15:51:30.019397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:30.005217Z","title":"AndroZoo: Collecting Millions of Android Apps for the Research Community,","venue":null,"work_id":"adc546f0-a95c-4ee1-a27c-a9f9de0a03b4","year":2016},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.715289Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:e574b7578caa29f7f1ddc1b1ddc86baf0d4badafde0b6f4911ad436a5801418e","observation_id":"41e06e0f-b7f5-4244-b712-27cae3766aa7","resolution":{"observed_at":"2026-08-05T15:51:30.008774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.06778","last_updated":"2022-10-31T17:58:37Z","snapshot_observed_at":"2026-08-13T14:06:55.964664Z","submitted_at":"2022-10-13T06:42:46Z","title":"X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation","version":2},"cited_work":{"arxiv_id":"2210.06778","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.06778","snapshot_observed_at":"2026-08-05T15:51:29.776930Z","title":"X-Align: Cross-Modal Cross-View Alignment for Bird's-Eye-View Segmentation","venue":"cs.CV","work_id":"51c58efe-3953-4d34-8a8d-da4062a5786c","year":2022},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.718101Z"},"links":{"cited_paper":"/paper/2210.06778","citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:c77bf10efda2170b404f5c438c492ac145bf97334374915a35136547d16c5966","observation_id":"43b60a96-f7ba-491d-bd42-b3693125724e","resolution":{"observed_at":"2026-08-05T15:51:29.780633Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:29.995096Z","title":"Realization of a quantum neural network using repeat-until- success circuits in a superconducting quantum processor,","venue":null,"work_id":"3e4b1e7a-5cdd-41a3-a237-d6120b1f8b7d","year":2023},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.721391Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:22e9663fc446b02bb7a54c1716bf18c52ff0fc9023c4e1d841326ff9d927a6d2","observation_id":"b1c4d9c1-9648-4e14-854c-496207bf8d58","resolution":{"observed_at":"2026-08-05T15:51:29.998671Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:29.985404Z","title":"A co-design framework of neural networks and quantum circuits towards quantum advantage,","venue":null,"work_id":"83871a5f-1913-4138-88fa-efcc6dcf734c","year":2021},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.724366Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:5153ad2aed73b09a87805e3a3faf13ded004034ac0fba08f85f7002dd79916ea","observation_id":"4ce36a2b-e541-4963-abae-21d62f67e345","resolution":{"observed_at":"2026-08-05T15:51:29.988771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:29.975596Z","title":"A lie algebraic theory of barren plateaus for deep parameterized quantum circuits,","venue":null,"work_id":"73714e0f-c7e2-4bed-8a22-8ba58e018c2f","year":2024},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.727328Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:1aa60c91e2326cbacc115f6bc730af69936edfa12fa1f141aaa965d5462c5ae8","observation_id":"d969ebba-fc3c-47f8-8837-22097ee0dce1","resolution":{"observed_at":"2026-08-05T15:51:29.979579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:29.965998Z","title":"Absence of barren plateaus in quantum convolutional neural networks,","venue":null,"work_id":"3e29ca74-f094-48c4-bb3a-b756d67a9741","year":2021},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.730170Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:47b3b3b987b37f72b8b597500e4b0326314d960b0722ad805fff672c1053056c","observation_id":"560fe483-8a46-496a-a0da-6fc327169081","resolution":{"observed_at":"2026-08-05T15:51:29.969532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-05T15:51:29.956655Z","title":"Towards explainable quantum machine learning for mobile malware detection and classification,","venue":null,"work_id":"b515421a-a12b-4a0d-81e1-d9c03fbafbd0","year":2022},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.733059Z"},"links":{"citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:692bac69a979ab6e3588bfb14f8f0924383eaaeea60045fbd5853e7c0608fb6c","observation_id":"5f9dfe08-a8f5-4819-9126-884cf5f8997d","resolution":{"observed_at":"2026-08-05T15:51:29.960035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.12161","last_updated":"2023-12-19T13:48:58Z","snapshot_observed_at":"2026-08-13T04:58:34.675077Z","submitted_at":"2023-12-19T13:48:58Z","title":"Towards an in-depth detection of malware using distributed QCNN","version":1},"cited_work":{"arxiv_id":"2312.12161","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.12161","snapshot_observed_at":"2026-08-05T15:51:29.761900Z","title":"Towards an in-depth detection of malware using distributed QCNN","venue":"cs.CR","work_id":"ab3e17fc-1a2e-4359-bf78-3ac226126cff","year":2023},"citing_paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:29.735938Z"},"links":{"cited_paper":"/paper/2312.12161","citing_paper":"/paper/2508.19381"},"observation_digest":"sha256:b1888797d383038bad37fc4248809fec26f86590870705467ebeed45da0e16f9","observation_id":"1a1c224d-8f1b-4388-883b-10a0354a8b07","resolution":{"observed_at":"2026-08-05T15:51:29.767471Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.19381","last_updated":"2025-08-26T19:20:21Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T15:13:55.005317Z","submitted_at":"2025-08-26T19:20:21Z","title":"Towards Quantum Machine Learning for Malicious Code Analysis"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":4,"verified_exact":3,"verified_fuzzy":25},"total_outbound_references":33},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 2 inbound Pith citation observations for arXiv:2508.19381."}