{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:4XFOFP67JONS2MHGCPA4L4M2YH","short_pith_number":"pith:4XFOFP67","schema_version":"1.0","canonical_sha256":"e5cae2bfdf4b9b2d30e613c1c5f19ac1d16fdb2f53d0cb16eb938b2532c4f87b","source":{"kind":"arxiv","id":"1708.00123","version":1},"attestation_state":"computed","paper":{"title":"Quantum Projective Simulation with Hamiltonian Evolution: A study in reinforcement learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Amara Katabarwa, Nima Karimatari","submitted_at":"2017-08-01T01:25:44Z","abstract_excerpt":"Projective Simulation was introduced as a novel approach to Artificial Intelligence. It involves a deliberation procedure that consists of a random walk on a graph of clips and allows for the learning agent to project itself into the future before committing to an action. Here we study and analyze a quantum mechanical version in which the random walk is performed by two kinds of Hamiltonians. The first kind is implemented by naively embedding the classical model in a quantum model by turning the clips into qubits. The other allows for storing clips in superpositions of qubits allowing for a po"},"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":"1708.00123","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2017-08-01T01:25:44Z","cross_cats_sorted":[],"title_canon_sha256":"0d0d2b7878d739f6abd6ec0dc274a7c2a8267253481c55b79e7b0c212348b51e","abstract_canon_sha256":"e4c6900c92ebe5eb12348fca42e4906c54764b209c9c8405dc4b7c7cd5489efe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:05.983421Z","signature_b64":"E8Oqr4R4sWbWFYEOeFTTtX3pZ4Z91lqPb1Qhzt0b2NDnJqf/g4weAoOjYP/vgvaeMLhqAMeSKjWwZDPDLGvvCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e5cae2bfdf4b9b2d30e613c1c5f19ac1d16fdb2f53d0cb16eb938b2532c4f87b","last_reissued_at":"2026-05-18T00:39:05.982772Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:05.982772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantum Projective Simulation with Hamiltonian Evolution: A study in reinforcement learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Amara Katabarwa, Nima Karimatari","submitted_at":"2017-08-01T01:25:44Z","abstract_excerpt":"Projective Simulation was introduced as a novel approach to Artificial Intelligence. It involves a deliberation procedure that consists of a random walk on a graph of clips and allows for the learning agent to project itself into the future before committing to an action. Here we study and analyze a quantum mechanical version in which the random walk is performed by two kinds of Hamiltonians. The first kind is implemented by naively embedding the classical model in a quantum model by turning the clips into qubits. The other allows for storing clips in superpositions of qubits allowing for a po"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.00123","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":""},"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":"1708.00123","created_at":"2026-05-18T00:39:05.982883+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.00123v1","created_at":"2026-05-18T00:39:05.982883+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.00123","created_at":"2026-05-18T00:39:05.982883+00:00"},{"alias_kind":"pith_short_12","alias_value":"4XFOFP67JONS","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_16","alias_value":"4XFOFP67JONS2MHG","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_8","alias_value":"4XFOFP67","created_at":"2026-05-18T12:31:00.734936+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4XFOFP67JONS2MHGCPA4L4M2YH","json":"https://pith.science/pith/4XFOFP67JONS2MHGCPA4L4M2YH.json","graph_json":"https://pith.science/api/pith-number/4XFOFP67JONS2MHGCPA4L4M2YH/graph.json","events_json":"https://pith.science/api/pith-number/4XFOFP67JONS2MHGCPA4L4M2YH/events.json","paper":"https://pith.science/paper/4XFOFP67"},"agent_actions":{"view_html":"https://pith.science/pith/4XFOFP67JONS2MHGCPA4L4M2YH","download_json":"https://pith.science/pith/4XFOFP67JONS2MHGCPA4L4M2YH.json","view_paper":"https://pith.science/paper/4XFOFP67","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.00123&json=true","fetch_graph":"https://pith.science/api/pith-number/4XFOFP67JONS2MHGCPA4L4M2YH/graph.json","fetch_events":"https://pith.science/api/pith-number/4XFOFP67JONS2MHGCPA4L4M2YH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4XFOFP67JONS2MHGCPA4L4M2YH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4XFOFP67JONS2MHGCPA4L4M2YH/action/storage_attestation","attest_author":"https://pith.science/pith/4XFOFP67JONS2MHGCPA4L4M2YH/action/author_attestation","sign_citation":"https://pith.science/pith/4XFOFP67JONS2MHGCPA4L4M2YH/action/citation_signature","submit_replication":"https://pith.science/pith/4XFOFP67JONS2MHGCPA4L4M2YH/action/replication_record"}},"created_at":"2026-05-18T00:39:05.982883+00:00","updated_at":"2026-05-18T00:39:05.982883+00:00"}