MAESTROCUT: Dynamic, Noise-Adaptive, and Secure Quantum Circuit Cutting on Near-Term Hardware

2025-08-31

AI Summary

Recent quantum hardware suffers from device drift and dynamic workload changes, degrading the accuracy of quantum circuit cutting. Method: This paper proposes a closed-loop, noise-adaptive quantum circuit cutting framework that integrates topology-aware resource allocation, dynamic partitioning, and noise-aware sampling scheduling. It introduces— for the first time—a cascaded online estimator coupled with a variance proxy mechanism to enable error-driven real-time recutting and adaptive reconstruction strategy selection. An online estimation algorithm combining maximum likelihood estimation (MLE), Bayesian inference, and Gaussian processes achieves error-adaptive control under a fixed measurement budget. Results: Experiments demonstrate over 40% reduction in reconstruction variance compared to baselines, significant mean squared error reduction, stable system latency, high reliability, and minimal software overhead (~1%).

Technology Category

• Machine Learning: Quantum Machine Learning
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Dynamic quantum circuit cutting adapts to device drift and workload variation
• Real-time variance tracking triggers re-cutting when accuracy degrades
• Online estimator cascade selects lowest-error reconstruction within fixed budget

Innovation

• Real-time variance tracking and adaptive re-cutting
• Online estimator cascade for low-error reconstruction
• Topology-aware shot routing with minimal software overhead