Near-term quantum workloads are shaped by coupled compilation and execution choices: qubit layout, routing, basis translation, gate suppression, measurement mitigation, shot budget, and artifact reproducibility. This paper analyzes QBalance, a Python workflow library for dataset-level selection among quantum compilation, noise-suppression, and error-mitigation strategies built on the Qiskit ecosystem. The contribution is formulated as a finite multi-objective strategy-selection problem over circuits, backends, and transformation policies. The manuscript derives the implemented weighted objective, non-dominated selection rule, survival-product error proxy, Bayesian linear candidate-ordering surrogate, and distributional diagnostics. It also positions the system relative to established work on Qiskit pass-manager compilation, SABRE-style routing, randomized compiling, dynamical decoupling, zero-noise extrapolation, matrix-free measurement mitigation, circuit cutting, and Thompson sampling. The analysis shows that QBalance provides a reproducible orchestration and artifact model for quantum workflow studies. It also establishes precise limitations: the current bandit mechanism orders candidates but does not reduce the number of candidate evaluations, the custom layout heuristic is greedy and only partially topology-aware, the implemented ZNE helper is parity-centered, and the cutting integration is a hook rather than a full reconstruction pipeline.

翻译：近期量子工作负载由编译与执行的耦合决策共同塑造：量子比特布局、路由、基变换、门抑制、测量缓解、采样预算以及工件可重现性。本文分析了基于Qiskit生态构建的Python工作流库QBalance，其面向数据集层面的量子编译、噪声抑制与错误缓解策略选择。该贡献被形式化为一个关于电路、后端及变换策略的有限多目标策略选择问题。本文推导了所实现的加权目标、非支配选择规则、存活乘积误差代理、贝叶斯线性候选排序替代模型及分布诊断方法，并将该系统与Qiskit通行管理器编译、SABRE式路由、随机编译、动态解耦、零噪声外推、无矩阵测量缓解、电路切割及Thompson采样等既有工作进行定位分析。研究表明QBalance为量子工作流研究提供了可重现的编排与工件模型，同时确立了明确的局限性：当前赌博机机制虽能排序候选方案但未减少候选评估次数、自定义布局启发式为贪心算法且仅部分感知拓扑结构、所实现的ZNE辅助器偏向奇偶性、切割集成仅为钩子而非完整重构管线。