{"paper":{"title":"Failure-Guided Fuzzing for Hybrid Quantum-Classical Programs","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Failure-guided local fuzzing drives better detection of non-convergent configurations in hybrid quantum-classical programs than random testing.","cross_cats":["quant-ph"],"primary_cat":"cs.SE","authors_text":"Lei Zhang","submitted_at":"2026-05-14T00:27:32Z","abstract_excerpt":"Hybrid quantum-classical (HQC) algorithms, such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA), are central to near-term quantum computing but remain challenging to test. Sampling-based fuzzing can expose faulty or non-convergent configurations, but under realistic execution budgets, it may miss failure-prone regions in the joint space of classical optimizer settings and quantum circuit parameters.\n  This paper studies failure-guided fuzzing for HQC programs. It models a hybrid input as a pair of classical optimizer hyperparameters and qu"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"failure-guided local fuzzing is the main driver of improvement over random testing, while concolic seed discovery provides additional benefits on VQE but is less stable on QAOA.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The two specific VQE and QAOA instances in Qiskit are representative of general HQC programs and that the chosen execution budgets reflect realistic constraints for failure detection.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Failure-guided local fuzzing around non-convergent seeds improves detection of faulty HQC configurations over random testing, with concolic seeding adding workload-dependent benefits on VQE versus QAOA.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Failure-guided local fuzzing drives better detection of non-convergent configurations in hybrid quantum-classical programs than random testing.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b3a2cc20c292e2f6faf68ab4cd30d640eb97c9768b144ca01850ecd2e5295d50"},"source":{"id":"2605.14219","kind":"arxiv","version":1},"verdict":{"id":"b9d04db9-b824-4b2e-ae68-1bc2756f675d","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T02:47:18.042138Z","strongest_claim":"failure-guided local fuzzing is the main driver of improvement over random testing, while concolic seed discovery provides additional benefits on VQE but is less stable on QAOA.","one_line_summary":"Failure-guided local fuzzing around non-convergent seeds improves detection of faulty HQC configurations over random testing, with concolic seeding adding workload-dependent benefits on VQE versus QAOA.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The two specific VQE and QAOA instances in Qiskit are representative of general HQC programs and that the chosen execution budgets reflect realistic constraints for failure detection.","pith_extraction_headline":"Failure-guided local fuzzing drives better detection of non-convergent configurations in hybrid quantum-classical programs than random testing."},"references":{"count":19,"sample":[{"doi":"","year":2022,"title":"The variational quantum eigensolver: a review of methods and best practices,","work_id":"3db30502-8acb-4276-8169-c8c0d1b8e772","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Quantum approximate optimization algo- rithm (qaoa),","work_id":"ea71e0ee-1d06-4418-8cdc-2ff36573d010","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Noisy intermediate-scale quantum algorithms,","work_id":"a8ce52f6-d7b2-482a-a816-ec465a5ddc7b","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Identifying flakiness in quantum programs,","work_id":"34212640-a6e9-489e-93de-b3a7e2c178e6","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Detecting flaky tests in quantum software: A dynamic approach,","work_id":"d07cb2bb-995c-42e9-94c8-192ee06c856b","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":19,"snapshot_sha256":"9495193e8fa0acf19ad099e9f09648d6711d318f6f494abc8d49cfe71748ddfd","internal_anchors":2},"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"}