{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7FM2ORB2K5V5RNJ3ROZ4FILPJF","short_pith_number":"pith:7FM2ORB2","schema_version":"1.0","canonical_sha256":"f959a7443a576bd8b53b8bb3c2a16f495f917e5b5790b442ace8679a02d202f6","source":{"kind":"arxiv","id":"2412.04053","version":3},"attestation_state":"computed","paper":{"title":"Enhanced Qubit Readout via Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Aniket Chatterjee, Jonathan Schwinger, Yvonne Y. Gao","submitted_at":"2024-12-05T10:43:36Z","abstract_excerpt":"Measurement is an essential component of robust and practical quantum computation. For superconducting qubits, the measurement process involves the effective manipulation of the joint qubit-resonator dynamics, and it should ideally provide the highest quality for qubit state discrimination with the shortest readout pulse and resonator reset time. Here, we harness model-free reinforcement learning (RL), together with a tailored training environment, to achieve this multi-pronged optimization task. Using the IBM quantum device, we demonstrate that the pulse obtained by the RL agent not only succ"},"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":"2412.04053","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2024-12-05T10:43:36Z","cross_cats_sorted":[],"title_canon_sha256":"6ed0d0def586b879d2ec2fb662fabe16111701499c6752822d238af1e1fb10cb","abstract_canon_sha256":"300d160ca4cd049304cb25421bae3c961a722a454d865bb8b53b3d4b732d21a2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:34:02.551712Z","signature_b64":"sEywWSWLJY07enhhe9O7uD83Qo881YyqWtAlNPmaNSmgEYoK8X/gJJW2e8VUW7JE1WHFzXVn/RBitT1+BYecAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f959a7443a576bd8b53b8bb3c2a16f495f917e5b5790b442ace8679a02d202f6","last_reissued_at":"2026-07-05T11:34:02.551357Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:34:02.551357Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhanced Qubit Readout via Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Aniket Chatterjee, Jonathan Schwinger, Yvonne Y. Gao","submitted_at":"2024-12-05T10:43:36Z","abstract_excerpt":"Measurement is an essential component of robust and practical quantum computation. For superconducting qubits, the measurement process involves the effective manipulation of the joint qubit-resonator dynamics, and it should ideally provide the highest quality for qubit state discrimination with the shortest readout pulse and resonator reset time. Here, we harness model-free reinforcement learning (RL), together with a tailored training environment, to achieve this multi-pronged optimization task. Using the IBM quantum device, we demonstrate that the pulse obtained by the RL agent not only succ"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04053","kind":"arxiv","version":3},"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/2412.04053/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":"2412.04053","created_at":"2026-07-05T11:34:02.551413+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.04053v3","created_at":"2026-07-05T11:34:02.551413+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04053","created_at":"2026-07-05T11:34:02.551413+00:00"},{"alias_kind":"pith_short_12","alias_value":"7FM2ORB2K5V5","created_at":"2026-07-05T11:34:02.551413+00:00"},{"alias_kind":"pith_short_16","alias_value":"7FM2ORB2K5V5RNJ3","created_at":"2026-07-05T11:34:02.551413+00:00"},{"alias_kind":"pith_short_8","alias_value":"7FM2ORB2","created_at":"2026-07-05T11:34:02.551413+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.14663","citing_title":"End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml","ref_index":46,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7FM2ORB2K5V5RNJ3ROZ4FILPJF","json":"https://pith.science/pith/7FM2ORB2K5V5RNJ3ROZ4FILPJF.json","graph_json":"https://pith.science/api/pith-number/7FM2ORB2K5V5RNJ3ROZ4FILPJF/graph.json","events_json":"https://pith.science/api/pith-number/7FM2ORB2K5V5RNJ3ROZ4FILPJF/events.json","paper":"https://pith.science/paper/7FM2ORB2"},"agent_actions":{"view_html":"https://pith.science/pith/7FM2ORB2K5V5RNJ3ROZ4FILPJF","download_json":"https://pith.science/pith/7FM2ORB2K5V5RNJ3ROZ4FILPJF.json","view_paper":"https://pith.science/paper/7FM2ORB2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.04053&json=true","fetch_graph":"https://pith.science/api/pith-number/7FM2ORB2K5V5RNJ3ROZ4FILPJF/graph.json","fetch_events":"https://pith.science/api/pith-number/7FM2ORB2K5V5RNJ3ROZ4FILPJF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7FM2ORB2K5V5RNJ3ROZ4FILPJF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7FM2ORB2K5V5RNJ3ROZ4FILPJF/action/storage_attestation","attest_author":"https://pith.science/pith/7FM2ORB2K5V5RNJ3ROZ4FILPJF/action/author_attestation","sign_citation":"https://pith.science/pith/7FM2ORB2K5V5RNJ3ROZ4FILPJF/action/citation_signature","submit_replication":"https://pith.science/pith/7FM2ORB2K5V5RNJ3ROZ4FILPJF/action/replication_record"}},"created_at":"2026-07-05T11:34:02.551413+00:00","updated_at":"2026-07-05T11:34:02.551413+00:00"}