{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:RKEK3EAV5S6JU3IBUKCUGMORM6","short_pith_number":"pith:RKEK3EAV","schema_version":"1.0","canonical_sha256":"8a88ad9015ecbc9a6d01a2854331d167a11e9673a3041bf593436184d3eff792","source":{"kind":"arxiv","id":"1905.10876","version":1},"attestation_state":"computed","paper":{"title":"Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Alan Aspuru-Guzik, Peter D. Johnson, Sukin Sim","submitted_at":"2019-05-26T21:02:08Z","abstract_excerpt":"Parameterized quantum circuits play an essential role in the performance of many variational hybrid quantum-classical (HQC) algorithms. One challenge in implementing such algorithms is to choose an effective circuit that well represents the solution space while maintaining a low circuit depth and number of parameters. To characterize and identify expressible, yet compact, parameterized circuits, we propose several descriptors, including measures of expressibility and entangling capability, that can be statistically estimated from classical simulations of parameterized quantum circuits. We comp"},"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":"1905.10876","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2019-05-26T21:02:08Z","cross_cats_sorted":[],"title_canon_sha256":"0dc1b2dca80f8d29317c683fbf6dd41a1ed4a11fee6d55b327177bef78a4e0a9","abstract_canon_sha256":"058085c992978055af2dc40890163d3f62c4b82374ebffc113ff4d6043c8f03b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:33:12.686273Z","signature_b64":"8qoevb16LHFpvaCOiW7u0zwfQX/cz3ykkiPfkdFLmtVE62f15qTm7hMqkNrnfGHoGrYnLs6QhmB1FWzkBQL8Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a88ad9015ecbc9a6d01a2854331d167a11e9673a3041bf593436184d3eff792","last_reissued_at":"2026-07-05T00:33:12.685841Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:33:12.685841Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Alan Aspuru-Guzik, Peter D. Johnson, Sukin Sim","submitted_at":"2019-05-26T21:02:08Z","abstract_excerpt":"Parameterized quantum circuits play an essential role in the performance of many variational hybrid quantum-classical (HQC) algorithms. One challenge in implementing such algorithms is to choose an effective circuit that well represents the solution space while maintaining a low circuit depth and number of parameters. To characterize and identify expressible, yet compact, parameterized circuits, we propose several descriptors, including measures of expressibility and entangling capability, that can be statistically estimated from classical simulations of parameterized quantum circuits. We comp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.10876","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/1905.10876/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":"1905.10876","created_at":"2026-07-05T00:33:12.685895+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.10876v1","created_at":"2026-07-05T00:33:12.685895+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.10876","created_at":"2026-07-05T00:33:12.685895+00:00"},{"alias_kind":"pith_short_12","alias_value":"RKEK3EAV5S6J","created_at":"2026-07-05T00:33:12.685895+00:00"},{"alias_kind":"pith_short_16","alias_value":"RKEK3EAV5S6JU3IB","created_at":"2026-07-05T00:33:12.685895+00:00"},{"alias_kind":"pith_short_8","alias_value":"RKEK3EAV","created_at":"2026-07-05T00:33:12.685895+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":27,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.05865","citing_title":"Symmetry-adapted qubit encoding with complete active space and Bravyi--Kitaev mapping for quantum chemistry on a quantum computer","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01253","citing_title":"Evaluating quantum circuits in the reservoir computing paradigm","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2606.31351","citing_title":"A Quantum-Classical Surrogate Model for the Collision Operator of the Lattice Boltzmann Method","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21286","citing_title":"Software Between Quantum and Machine Learning -- And Down to Pulses","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28927","citing_title":"Quantum encodings that preserve persistent homology","ref_index":110,"is_internal_anchor":false},{"citing_arxiv_id":"2606.01291","citing_title":"Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2606.01110","citing_title":"Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2606.05387","citing_title":"Feature Encoding in Quantum Machine Learning: A Survey and Practical Guidelines","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2606.24932","citing_title":"Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2606.25598","citing_title":"The Cost of Removing Tunability in Quantum Data Re-Uploading","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2404.19497","citing_title":"Light Cone Cancellation for Variational Quantum Eigensolver in Solving Noisy Max-Cut","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21286","citing_title":"Software Between Quantum and Machine Learning -- And Down to Pulses","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17587","citing_title":"Large-Scale Quantum Kernels for Hyperspectral Data Classification","ref_index":71,"is_internal_anchor":false},{"citing_arxiv_id":"2605.18540","citing_title":"Discovering Data Encoding Strategies for Quantum-Classical Neural Networks Using Monte Carlo Tree Search","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2507.23679","citing_title":"Swap Network Augmented Ans\\\"atze on Arbitrary Connectivity","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2508.00768","citing_title":"Evaluating Angle and Amplitude Encoding Strategies for Variational Quantum Machine Learning: their impact on model's accuracy","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2510.14099","citing_title":"A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics","ref_index":92,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01253","citing_title":"Evaluating quantum circuits in the reservoir computing paradigm","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07611","citing_title":"Compositional Quantum Heuristics for Max-Clique Detection","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04414","citing_title":"Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18457","citing_title":"Random-State Generation and Preparation Complexity in Rydberg Atom Arrays","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18495","citing_title":"Scaling of Quantum Resources for Simulating a Long-Range System","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20961","citing_title":"Ans\\\"atz Expressivity and Optimization in Variational Quantum Simulations of Transverse-field Ising Model Across System Sizes","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02850","citing_title":"Quantum Tilted Loss in Variational Optimization: Theory and Applications","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2605.03434","citing_title":"Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RKEK3EAV5S6JU3IBUKCUGMORM6","json":"https://pith.science/pith/RKEK3EAV5S6JU3IBUKCUGMORM6.json","graph_json":"https://pith.science/api/pith-number/RKEK3EAV5S6JU3IBUKCUGMORM6/graph.json","events_json":"https://pith.science/api/pith-number/RKEK3EAV5S6JU3IBUKCUGMORM6/events.json","paper":"https://pith.science/paper/RKEK3EAV"},"agent_actions":{"view_html":"https://pith.science/pith/RKEK3EAV5S6JU3IBUKCUGMORM6","download_json":"https://pith.science/pith/RKEK3EAV5S6JU3IBUKCUGMORM6.json","view_paper":"https://pith.science/paper/RKEK3EAV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.10876&json=true","fetch_graph":"https://pith.science/api/pith-number/RKEK3EAV5S6JU3IBUKCUGMORM6/graph.json","fetch_events":"https://pith.science/api/pith-number/RKEK3EAV5S6JU3IBUKCUGMORM6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RKEK3EAV5S6JU3IBUKCUGMORM6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RKEK3EAV5S6JU3IBUKCUGMORM6/action/storage_attestation","attest_author":"https://pith.science/pith/RKEK3EAV5S6JU3IBUKCUGMORM6/action/author_attestation","sign_citation":"https://pith.science/pith/RKEK3EAV5S6JU3IBUKCUGMORM6/action/citation_signature","submit_replication":"https://pith.science/pith/RKEK3EAV5S6JU3IBUKCUGMORM6/action/replication_record"}},"created_at":"2026-07-05T00:33:12.685895+00:00","updated_at":"2026-07-05T00:33:12.685895+00:00"}