{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:FQDI6YSZK33RI67WW5VEC336D6","short_pith_number":"pith:FQDI6YSZ","canonical_record":{"source":{"id":"2210.13200","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2022-10-24T13:23:36Z","cross_cats_sorted":[],"title_canon_sha256":"a00a96f59b52a459512e4e4d493519057991393d605b6584e29eae215ada3d34","abstract_canon_sha256":"bbbe980d93ed72eb3211ed6b5626552eafd32cf870cacc64e54b5222c37fd476"},"schema_version":"1.0"},"canonical_sha256":"2c068f625956f7147bf6b76a416f7e1fbebfb93687bdff5d3a9fd50feeaff073","source":{"kind":"arxiv","id":"2210.13200","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.13200","created_at":"2026-07-05T05:09:37Z"},{"alias_kind":"arxiv_version","alias_value":"2210.13200v1","created_at":"2026-07-05T05:09:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.13200","created_at":"2026-07-05T05:09:37Z"},{"alias_kind":"pith_short_12","alias_value":"FQDI6YSZK33R","created_at":"2026-07-05T05:09:37Z"},{"alias_kind":"pith_short_16","alias_value":"FQDI6YSZK33RI67W","created_at":"2026-07-05T05:09:37Z"},{"alias_kind":"pith_short_8","alias_value":"FQDI6YSZ","created_at":"2026-07-05T05:09:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:FQDI6YSZK33RI67WW5VEC336D6","target":"record","payload":{"canonical_record":{"source":{"id":"2210.13200","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2022-10-24T13:23:36Z","cross_cats_sorted":[],"title_canon_sha256":"a00a96f59b52a459512e4e4d493519057991393d605b6584e29eae215ada3d34","abstract_canon_sha256":"bbbe980d93ed72eb3211ed6b5626552eafd32cf870cacc64e54b5222c37fd476"},"schema_version":"1.0"},"canonical_sha256":"2c068f625956f7147bf6b76a416f7e1fbebfb93687bdff5d3a9fd50feeaff073","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:09:37.257738Z","signature_b64":"n68p1cPZQFJP4IJRcIUms61ij4EgUowfvz/TuLWMBuZs+4By8Dwi/Z7aZc9QSQJD9Lyv/SVTX7DEIQ3suWB9Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c068f625956f7147bf6b76a416f7e1fbebfb93687bdff5d3a9fd50feeaff073","last_reissued_at":"2026-07-05T05:09:37.257358Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:09:37.257358Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.13200","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:09:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZaE0DhYMeZYBcLuqotJz3HhtDwbUpl3VkvoKgz9+yfEyE/waGoQiLsB7MAL8e+CFpO1lkrCRhTjTR/a+k964Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:51:55.870279Z"},"content_sha256":"67d76a91d2e84ba25a18f55ba377dd99dbe728d138e09de8df0182644e32858a","schema_version":"1.0","event_id":"sha256:67d76a91d2e84ba25a18f55ba377dd99dbe728d138e09de8df0182644e32858a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:FQDI6YSZK33RI67WW5VEC336D6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Classically Approximating Variational Quantum Machine Learning with Random Fourier Features","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Constantin Dalyac, Elham Kashefi, Hela Mhiri, Jonas Landman, Slimane Thabet","submitted_at":"2022-10-24T13:23:36Z","abstract_excerpt":"Many applications of quantum computing in the near term rely on variational quantum circuits (VQCs). They have been showcased as a promising model for reaching a quantum advantage in machine learning with current noisy intermediate scale quantum computers (NISQ). It is often believed that the power of VQCs relies on their exponentially large feature space, and extensive works have explored the expressiveness and trainability of VQCs in that regard. In our work, we propose a classical sampling method that may closely approximate a VQC with Hamiltonian encoding, given only the description of its"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.13200","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2210.13200/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:09:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QPBhMWWKRU6g1oHG51tVugED0nQ6FxveULFfvj6ilFwhIv2p6NzkPiOGd5e7jiddEXHEdDijFUwaxHJo6+aeDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:51:55.870786Z"},"content_sha256":"956f1781194e2ab0467053062936eb3c7db4474445320768985b961a80a538e5","schema_version":"1.0","event_id":"sha256:956f1781194e2ab0467053062936eb3c7db4474445320768985b961a80a538e5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FQDI6YSZK33RI67WW5VEC336D6/bundle.json","state_url":"https://pith.science/pith/FQDI6YSZK33RI67WW5VEC336D6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FQDI6YSZK33RI67WW5VEC336D6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T23:51:55Z","links":{"resolver":"https://pith.science/pith/FQDI6YSZK33RI67WW5VEC336D6","bundle":"https://pith.science/pith/FQDI6YSZK33RI67WW5VEC336D6/bundle.json","state":"https://pith.science/pith/FQDI6YSZK33RI67WW5VEC336D6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FQDI6YSZK33RI67WW5VEC336D6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:FQDI6YSZK33RI67WW5VEC336D6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"bbbe980d93ed72eb3211ed6b5626552eafd32cf870cacc64e54b5222c37fd476","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2022-10-24T13:23:36Z","title_canon_sha256":"a00a96f59b52a459512e4e4d493519057991393d605b6584e29eae215ada3d34"},"schema_version":"1.0","source":{"id":"2210.13200","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.13200","created_at":"2026-07-05T05:09:37Z"},{"alias_kind":"arxiv_version","alias_value":"2210.13200v1","created_at":"2026-07-05T05:09:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.13200","created_at":"2026-07-05T05:09:37Z"},{"alias_kind":"pith_short_12","alias_value":"FQDI6YSZK33R","created_at":"2026-07-05T05:09:37Z"},{"alias_kind":"pith_short_16","alias_value":"FQDI6YSZK33RI67W","created_at":"2026-07-05T05:09:37Z"},{"alias_kind":"pith_short_8","alias_value":"FQDI6YSZ","created_at":"2026-07-05T05:09:37Z"}],"graph_snapshots":[{"event_id":"sha256:956f1781194e2ab0467053062936eb3c7db4474445320768985b961a80a538e5","target":"graph","created_at":"2026-07-05T05:09:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2210.13200/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many applications of quantum computing in the near term rely on variational quantum circuits (VQCs). They have been showcased as a promising model for reaching a quantum advantage in machine learning with current noisy intermediate scale quantum computers (NISQ). It is often believed that the power of VQCs relies on their exponentially large feature space, and extensive works have explored the expressiveness and trainability of VQCs in that regard. In our work, we propose a classical sampling method that may closely approximate a VQC with Hamiltonian encoding, given only the description of its","authors_text":"Constantin Dalyac, Elham Kashefi, Hela Mhiri, Jonas Landman, Slimane Thabet","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2022-10-24T13:23:36Z","title":"Classically Approximating Variational Quantum Machine Learning with Random Fourier Features"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.13200","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:67d76a91d2e84ba25a18f55ba377dd99dbe728d138e09de8df0182644e32858a","target":"record","created_at":"2026-07-05T05:09:37Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"bbbe980d93ed72eb3211ed6b5626552eafd32cf870cacc64e54b5222c37fd476","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2022-10-24T13:23:36Z","title_canon_sha256":"a00a96f59b52a459512e4e4d493519057991393d605b6584e29eae215ada3d34"},"schema_version":"1.0","source":{"id":"2210.13200","kind":"arxiv","version":1}},"canonical_sha256":"2c068f625956f7147bf6b76a416f7e1fbebfb93687bdff5d3a9fd50feeaff073","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2c068f625956f7147bf6b76a416f7e1fbebfb93687bdff5d3a9fd50feeaff073","first_computed_at":"2026-07-05T05:09:37.257358Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:09:37.257358Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"n68p1cPZQFJP4IJRcIUms61ij4EgUowfvz/TuLWMBuZs+4By8Dwi/Z7aZc9QSQJD9Lyv/SVTX7DEIQ3suWB9Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T05:09:37.257738Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.13200","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:67d76a91d2e84ba25a18f55ba377dd99dbe728d138e09de8df0182644e32858a","sha256:956f1781194e2ab0467053062936eb3c7db4474445320768985b961a80a538e5"],"state_sha256":"f663e28edff45adbcc680bbb04baf7439f4dbbf7098c4cea821fd0a4bc568d44"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6iDPVkOtbCtLOltSA6w6YtuQ1P1wJWCy1Xu9i4eVdmdRuROaQYFFwQc26CjT51tB2ohtdBSW+lHohMeTbP4FCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T23:51:55.875588Z","bundle_sha256":"706fc7b57664dee5d35bbcd140cbc427d4c643a6dbfbaba63341b62a32f0dcff"}}