{"ID":2876230,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.00811","arxiv_id":"2509.00811","title":"MAESTROCUT: Dynamic, Noise-Adaptive, and Secure Quantum Circuit Cutting on Near-Term Hardware","abstract":"We present MaestroCut, a closed-loop framework for quantum circuit cutting that adapts partitioning and shot allocation to device drift and workload variation. MaestroCut tracks a variance proxy in real time, triggers re-cutting when accuracy degrades, and routes shots using topology-aware priors. An online estimator cascade (MLE, Bayesian, GP-assisted) selects the lowest-error reconstruction within a fixed budget. Tier-1 simulations show consistent variance contraction and reduced mean-squared error versus uniform and proportional baselines. Tier-2 emulation with realistic queueing and noise demonstrates stable latency targets, high reliability, and ~1% software overhead under stress scenarios. These results indicate that adaptive circuit cutting can provide accuracy and efficiency improvements with minimal operational cost on near-term hardware.","short_abstract":"We present MaestroCut, a closed-loop framework for quantum circuit cutting that adapts partitioning and shot allocation to device drift and workload variation. MaestroCut tracks a variance proxy in real time, triggers re-cutting when accuracy degrades, and routes shots using topology-aware priors. An online estimator c...","url_abs":"https://arxiv.org/abs/2509.00811","url_pdf":"https://arxiv.org/pdf/2509.00811v1","authors":"[\"Samuel Punch\",\"Krishnendu Guha\"]","published":"2025-08-31T12:01:16Z","proceeding":"cs.CR","tasks":"[\"cs.CR\"]","methods":"[]","has_code":false}