{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:AXKUHAXYN45PAUS3XSNGLLZABM","short_pith_number":"pith:AXKUHAXY","canonical_record":{"source":{"id":"2605.13979","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-13T18:00:56Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"776852bf12ce2a958cddb35115074cf35b13dc06db0777fda00e7c12eedef9c5","abstract_canon_sha256":"38bd54ce92d4d4b20af1ef795ef72a3dac349e3f99d6868013d166a7de00c4c0"},"schema_version":"1.0"},"canonical_sha256":"05d54382f86f3af0525bbc9a65af200b2661f26e02e507b3408e766395b3695f","source":{"kind":"arxiv","id":"2605.13979","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.13979","created_at":"2026-05-17T23:39:13Z"},{"alias_kind":"arxiv_version","alias_value":"2605.13979v1","created_at":"2026-05-17T23:39:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13979","created_at":"2026-05-17T23:39:13Z"},{"alias_kind":"pith_short_12","alias_value":"AXKUHAXYN45P","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"AXKUHAXYN45PAUS3","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"AXKUHAXY","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:AXKUHAXYN45PAUS3XSNGLLZABM","target":"record","payload":{"canonical_record":{"source":{"id":"2605.13979","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-13T18:00:56Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"776852bf12ce2a958cddb35115074cf35b13dc06db0777fda00e7c12eedef9c5","abstract_canon_sha256":"38bd54ce92d4d4b20af1ef795ef72a3dac349e3f99d6868013d166a7de00c4c0"},"schema_version":"1.0"},"canonical_sha256":"05d54382f86f3af0525bbc9a65af200b2661f26e02e507b3408e766395b3695f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:13.398374Z","signature_b64":"UHm386SX9ZDQcAjzwUzf2a2lFctCxsxRpNFrBWfUyBs13mUetYxlSFDYuAx56YqIOu5cZ4/xYAav+3HF5aT+DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"05d54382f86f3af0525bbc9a65af200b2661f26e02e507b3408e766395b3695f","last_reissued_at":"2026-05-17T23:39:13.397630Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:13.397630Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.13979","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":"2026-05-17T23:39:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tWEDsbuimGA+x+zwf/vs3hdZV1nc5yjR75ZtAHP7H6TABrImVObDZGCr6BWRBx3RqHyibhgR6Om2iHNsYC4pBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T16:07:46.716024Z"},"content_sha256":"a4df2733061aa8302ae94b91a99933b4379bfc656de9d1afd462d7fbf20300b7","schema_version":"1.0","event_id":"sha256:a4df2733061aa8302ae94b91a99933b4379bfc656de9d1afd462d7fbf20300b7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:AXKUHAXYN45PAUS3XSNGLLZABM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Winning Lottery Tickets in Neural Networks via a Quantum-Inspired Classical Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A classical algorithm samples from a ridgelet-defined distribution to select sparse neural subnetworks in polynomial time in data dimension D.","cross_cats":["cs.LG","stat.ML"],"primary_cat":"quant-ph","authors_text":"Hayata Yamasaki, Mio Murao, Natsuto Isogai, Sho Sonoda","submitted_at":"2026-05-13T18:00:56Z","abstract_excerpt":"Quantum machine learning (QML) aims to accelerate machine learning tasks by exploiting quantum computation. Previous work studied a QML algorithm for selecting sparse subnetworks from large shallow neural networks. Instead of directly solving an optimization problem over a large-scale network, this algorithm constructs a sparse subnetwork by sampling hidden nodes from an optimized probability distribution defined using the ridgelet transform. The quantum algorithm performs this sampling in time $O(D)$ in the data dimension $D$, whereas a naive classical implementation relies on handling expone"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We show that our algorithm runs in time O(poly(D)), thereby removing the exponential dependence on D from the previous classical approach.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The ridgelet transform defines an optimized probability distribution that admits an efficient classical sampling procedure with only polynomial dependence on the data dimension D.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A classical polynomial-time algorithm for optimized sampling of lottery tickets in neural networks removes the exponential dependence on data dimension from prior classical approaches.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A classical algorithm samples from a ridgelet-defined distribution to select sparse neural subnetworks in polynomial time in data dimension D.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"88c6b0ff083a62cad5f6b0524b093abe171912ae2abbad588e539b882510bb62"},"source":{"id":"2605.13979","kind":"arxiv","version":1},"verdict":{"id":"06cfe008-dc72-4ad9-a09b-c29825a29f5d","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T05:50:22.825489Z","strongest_claim":"We show that our algorithm runs in time O(poly(D)), thereby removing the exponential dependence on D from the previous classical approach.","one_line_summary":"A classical polynomial-time algorithm for optimized sampling of lottery tickets in neural networks removes the exponential dependence on data dimension from prior classical approaches.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The ridgelet transform defines an optimized probability distribution that admits an efficient classical sampling procedure with only polynomial dependence on the data dimension D.","pith_extraction_headline":"A classical algorithm samples from a ridgelet-defined distribution to select sparse neural subnetworks in polynomial time in data dimension D."},"references":{"count":33,"sample":[{"doi":"","year":2016,"title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","work_id":"d79f6a26-2d92-4f7c-aea0-8aabf3b668c8","ref_index":1,"cited_arxiv_id":"1510.00149","is_internal_anchor":true},{"doi":"","year":2019,"title":"SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY","work_id":"3560a2b3-dfa6-4c6f-b2aa-48b7fa8073e9","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, and Surya Ganguli. Pruning neural networks without any data by iteratively conserving synaptic flow. InProceedings of the 34th International Confere","work_id":"7e8f9941-2c8e-41f4-b814-0f899becce46","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Rigging the lottery: making all tickets winners","work_id":"fdc66ad3-07d5-4549-8aa8-a4d36d1bfc06","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks","work_id":"fd1ceaf9-3775-4154-bf6f-c2c75641e98f","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":33,"snapshot_sha256":"19c4558597966f53219c4b6e20389055eb68d3774912bd268616d24b5094b0d5","internal_anchors":3},"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":"06cfe008-dc72-4ad9-a09b-c29825a29f5d"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A+iZiaYYjzmRf/W9toXi/KvzEIuJwBMCZ26PfuSpGBEtLiQyElclXhE9mEGiRPv3AElVPOzqEKKe1mMHGdy4CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T16:07:46.716591Z"},"content_sha256":"f3f34cba43fd16678fa5b289a81a8dddf542fade5a37d9f83f3bf832fef0b7cb","schema_version":"1.0","event_id":"sha256:f3f34cba43fd16678fa5b289a81a8dddf542fade5a37d9f83f3bf832fef0b7cb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AXKUHAXYN45PAUS3XSNGLLZABM/bundle.json","state_url":"https://pith.science/pith/AXKUHAXYN45PAUS3XSNGLLZABM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AXKUHAXYN45PAUS3XSNGLLZABM/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":"2026-05-30T16:07:46Z","links":{"resolver":"https://pith.science/pith/AXKUHAXYN45PAUS3XSNGLLZABM","bundle":"https://pith.science/pith/AXKUHAXYN45PAUS3XSNGLLZABM/bundle.json","state":"https://pith.science/pith/AXKUHAXYN45PAUS3XSNGLLZABM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AXKUHAXYN45PAUS3XSNGLLZABM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:AXKUHAXYN45PAUS3XSNGLLZABM","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":"38bd54ce92d4d4b20af1ef795ef72a3dac349e3f99d6868013d166a7de00c4c0","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-13T18:00:56Z","title_canon_sha256":"776852bf12ce2a958cddb35115074cf35b13dc06db0777fda00e7c12eedef9c5"},"schema_version":"1.0","source":{"id":"2605.13979","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.13979","created_at":"2026-05-17T23:39:13Z"},{"alias_kind":"arxiv_version","alias_value":"2605.13979v1","created_at":"2026-05-17T23:39:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13979","created_at":"2026-05-17T23:39:13Z"},{"alias_kind":"pith_short_12","alias_value":"AXKUHAXYN45P","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"AXKUHAXYN45PAUS3","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"AXKUHAXY","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:f3f34cba43fd16678fa5b289a81a8dddf542fade5a37d9f83f3bf832fef0b7cb","target":"graph","created_at":"2026-05-17T23:39:13Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We show that our algorithm runs in time O(poly(D)), thereby removing the exponential dependence on D from the previous classical approach."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The ridgelet transform defines an optimized probability distribution that admits an efficient classical sampling procedure with only polynomial dependence on the data dimension D."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A classical polynomial-time algorithm for optimized sampling of lottery tickets in neural networks removes the exponential dependence on data dimension from prior classical approaches."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A classical algorithm samples from a ridgelet-defined distribution to select sparse neural subnetworks in polynomial time in data dimension D."}],"snapshot_sha256":"88c6b0ff083a62cad5f6b0524b093abe171912ae2abbad588e539b882510bb62"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Quantum machine learning (QML) aims to accelerate machine learning tasks by exploiting quantum computation. Previous work studied a QML algorithm for selecting sparse subnetworks from large shallow neural networks. Instead of directly solving an optimization problem over a large-scale network, this algorithm constructs a sparse subnetwork by sampling hidden nodes from an optimized probability distribution defined using the ridgelet transform. The quantum algorithm performs this sampling in time $O(D)$ in the data dimension $D$, whereas a naive classical implementation relies on handling expone","authors_text":"Hayata Yamasaki, Mio Murao, Natsuto Isogai, Sho Sonoda","cross_cats":["cs.LG","stat.ML"],"headline":"A classical algorithm samples from a ridgelet-defined distribution to select sparse neural subnetworks in polynomial time in data dimension D.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-13T18:00:56Z","title":"Winning Lottery Tickets in Neural Networks via a Quantum-Inspired Classical Algorithm"},"references":{"count":33,"internal_anchors":3,"resolved_work":33,"sample":[{"cited_arxiv_id":"1510.00149","doi":"","is_internal_anchor":true,"ref_index":1,"title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","work_id":"d79f6a26-2d92-4f7c-aea0-8aabf3b668c8","year":2016},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY","work_id":"3560a2b3-dfa6-4c6f-b2aa-48b7fa8073e9","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, and Surya Ganguli. Pruning neural networks without any data by iteratively conserving synaptic flow. InProceedings of the 34th International Confere","work_id":"7e8f9941-2c8e-41f4-b814-0f899becce46","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Rigging the lottery: making all tickets winners","work_id":"fdc66ad3-07d5-4549-8aa8-a4d36d1bfc06","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks","work_id":"fd1ceaf9-3775-4154-bf6f-c2c75641e98f","year":2019}],"snapshot_sha256":"19c4558597966f53219c4b6e20389055eb68d3774912bd268616d24b5094b0d5"},"source":{"id":"2605.13979","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T05:50:22.825489Z","id":"06cfe008-dc72-4ad9-a09b-c29825a29f5d","model_set":{"reader":"grok-4.3"},"one_line_summary":"A classical polynomial-time algorithm for optimized sampling of lottery tickets in neural networks removes the exponential dependence on data dimension from prior classical approaches.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A classical algorithm samples from a ridgelet-defined distribution to select sparse neural subnetworks in polynomial time in data dimension D.","strongest_claim":"We show that our algorithm runs in time O(poly(D)), thereby removing the exponential dependence on D from the previous classical approach.","weakest_assumption":"The ridgelet transform defines an optimized probability distribution that admits an efficient classical sampling procedure with only polynomial dependence on the data dimension D."}},"verdict_id":"06cfe008-dc72-4ad9-a09b-c29825a29f5d"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a4df2733061aa8302ae94b91a99933b4379bfc656de9d1afd462d7fbf20300b7","target":"record","created_at":"2026-05-17T23:39:13Z","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":"38bd54ce92d4d4b20af1ef795ef72a3dac349e3f99d6868013d166a7de00c4c0","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2026-05-13T18:00:56Z","title_canon_sha256":"776852bf12ce2a958cddb35115074cf35b13dc06db0777fda00e7c12eedef9c5"},"schema_version":"1.0","source":{"id":"2605.13979","kind":"arxiv","version":1}},"canonical_sha256":"05d54382f86f3af0525bbc9a65af200b2661f26e02e507b3408e766395b3695f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"05d54382f86f3af0525bbc9a65af200b2661f26e02e507b3408e766395b3695f","first_computed_at":"2026-05-17T23:39:13.397630Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:13.397630Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UHm386SX9ZDQcAjzwUzf2a2lFctCxsxRpNFrBWfUyBs13mUetYxlSFDYuAx56YqIOu5cZ4/xYAav+3HF5aT+DA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:13.398374Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.13979","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a4df2733061aa8302ae94b91a99933b4379bfc656de9d1afd462d7fbf20300b7","sha256:f3f34cba43fd16678fa5b289a81a8dddf542fade5a37d9f83f3bf832fef0b7cb"],"state_sha256":"5f71778665ce4832db596679f7807c64d35658c85658cd9734b82b74c8af4d19"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fUSVtsfaeyUbwUx9kpJx0eU/ZhWGdpSmCV+YXXhjbiHZqXm2NU//8HrkHruo2tb/u7smeIuLUW9xlZmKpfj9Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T16:07:46.719362Z","bundle_sha256":"4d9cc81c09f0d59ef6c358242889a618fb6b953846c557470cc80fcd286684c7"}}