Quantum error mitigation (QEM) is essential for extracting reliable results from near-term quantum devices, yet practical deployments must balance mitigation strength against runtime overhead under time-varying noise. We introduce \emph{GSC-QEMit}, a telemetry-driven, \textbf{context--forecast--bandit} framework for \emph{adaptive} mitigation that switches between lightweight suppression and heavier intervention as drift evolves. GSC-QEMit composes three coupled modules: (G) a Growing Hierarchical Self-Organizing Map (GHSOM) that clusters streaming telemetry into operating contexts; (S) an uncertainty-aware subsampled Gaussian-process forecaster that predicts short-horizon fidelity degradation; and (C) a cost-aware contextual multi-armed bandit (CMAB) that selects mitigation actions via Thompson sampling with explicit intervention cost. We evaluate GSC-QEMit on benchmark circuit families (GHZ, Quantum Fourier Transform, and Grover search) under nonstationary noise regimes simulated in Qiskit Aer, using an instrumented testbed where action labels correspond to graded mitigation intensity. Across Clifford, non-Clifford, and structured workloads, GSC-QEMit improves average logical fidelity by \textbf{+9.0\%} relative to unmitigated execution while reducing unnecessary heavy interventions by reserving them for inferred noise spikes. The resulting policies exhibit a favorable fidelity--cost trade-off and transfer across the evaluated workloads without circuit-specific tuning.

翻译：量子错误缓解（QEM）对于从近期的量子设备中提取可靠结果至关重要，然而实际部署必须在时变噪声下平衡缓解强度与运行时开销。我们提出*GSC-QEMit*，一种遥测驱动的、**上下文-预测-多臂赌博机**框架，用于*自适应*缓解，随着噪声漂移在轻量级抑制和较重干预之间切换。GSC-QEMit由三个耦合模块组成:（G）一种增长型分层自组织映射图（GHSOM），将流式遥测数据聚类为操作上下文；（S）一种不确定性感知的二次采样高斯过程预测器，用于预测短时间内的保真度退化；（C）一种成本感知的上下文多臂赌博机（CMAB），通过显式干预成本的汤普森采样选择缓解操作。我们在Qiskit Aer模拟的非平稳噪声环境下，使用标注了分级缓解强度的仪表化测试平台，对基准电路系列（GHZ、量子傅里叶变换和Grover搜索）评估了GSC-QEMit。在克利福德、非克利福德和结构化工作负载中，与未缓解的执行相比，GSC-QEMit将平均逻辑保真度提高了**+9.0%**，同时通过将重型干预保留用于推断出的噪声尖峰来减少不必要的重型干预。所得策略表现出有利的保真度-成本权衡，并且无需电路特定调优即可在评估的工作负载间迁移。