{"paper":{"title":"Accelerating State-Vector Quantum Simulation on Integrated GPUs via Cache Locality Optimization: A Cross-Architecture Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Reorganizing the quantum state vector for last-level cache locality reverses GPU performance degradation on integrated hardware as qubit count grows.","cross_cats":["cs.AR","cs.DC","cs.PF"],"primary_cat":"quant-ph","authors_text":"Eduarda Rodrigues Monteiro, Evandro Chagas Ribeiro da Rosa, Fernando Augusto Caletti de Barros, Gabriel Fernandes Thomaz, Jerusa Marchi","submitted_at":"2026-05-14T17:17:02Z","abstract_excerpt":"The classical simulation of quantum algorithms is a crucial tool for circuit development, testing, and validation. Although acceleration using GPUs significantly reduces simulation time, most high-performance simulators rely on vendor-specific frameworks that target data-center hardware. To broaden access to quantum simulation, this work proposes a vendor-agnostic approach targeting the integrated GPUs commonly found in consumer-grade laptops. A primary challenge in state-vector simulation is its inherently poor spatial locality, which creates a memory bandwidth bottleneck. Consequently, basel"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"the proposed optimization successfully mitigates performance degradation at larger qubit scales. In particular, for a 28-qubit simulation, the optimization reversed a performance deficit on an Intel Core i5, improving the GPU speedup over the CPU from 0.95x to 1.89x, and increased the Apple M1 Pro speedup from 3.71x to 5.88x.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the state partitioning reorganization can be performed with negligible overhead and that the resulting cache behavior remains consistent across diverse integrated GPU architectures without architecture-specific tuning.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"State partitioning optimization improves cache locality and reverses GPU performance deficits for 28-qubit quantum state-vector simulations on integrated GPUs.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Reorganizing the quantum state vector for last-level cache locality reverses GPU performance degradation on integrated hardware as qubit count grows.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"9951763dfbdbe38c022831f2d4188338b45ff0af477e44cb5484e355ed4040e9"},"source":{"id":"2605.15098","kind":"arxiv","version":1},"verdict":{"id":"63ef5013-3c2a-4bb3-939b-04f2cbeb710d","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T03:07:11.695368Z","strongest_claim":"the proposed optimization successfully mitigates performance degradation at larger qubit scales. In particular, for a 28-qubit simulation, the optimization reversed a performance deficit on an Intel Core i5, improving the GPU speedup over the CPU from 0.95x to 1.89x, and increased the Apple M1 Pro speedup from 3.71x to 5.88x.","one_line_summary":"State partitioning optimization improves cache locality and reverses GPU performance deficits for 28-qubit quantum state-vector simulations on integrated GPUs.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the state partitioning reorganization can be performed with negligible overhead and that the resulting cache behavior remains consistent across diverse integrated GPU architectures without architecture-specific tuning.","pith_extraction_headline":"Reorganizing the quantum state vector for last-level cache locality reverses GPU performance degradation on integrated hardware as qubit count grows."},"references":{"count":14,"sample":[{"doi":"","year":2025,"title":"Quantum Software Engineering: Roadmap and Chal- lenges Ahead,","work_id":"aff14af5-e87e-453d-a648-59c9d4216aec","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"Quantum Computing in the NISQ era and beyond,","work_id":"f3d5423c-5324-4b46-a490-c74e72d74168","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"Simulation of Quantum Computers: Review and Acceleration Oppor- tunities,","work_id":"f630f667-bd9e-4b0f-9885-b99b73ec8c4f","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1997,"title":"Stabilizer Codes and Quantum Error Correction,","work_id":"20cfbf22-7d35-40af-ab4e-cd0ce600c7ce","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2016,"title":"qHiPSTER: The Quantum High Performance Software Testing Environment,","work_id":"b628ee3c-a754-42ca-b64c-eabfa7a0eded","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":14,"snapshot_sha256":"b52290a4474c6d47162438505dfe9dd7303998960d2d801fd75646154a5288c5","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"}