发表机构
University of Utah; Rice University（犹他大学; 莱斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
BAHAMAS是一种在线控制框架，通过自适应选择物理映射稳定变分量子算法的优化，可提升真实量子设备上的优化可靠性并支持漂移下的推理时重定向。
AI 中文摘要
变分量子算法（VQA）因时间噪声漂移和静态量子比特映射导致梯度信号在迭代间失真，优化稳定性差。我们提出BAHAMAS，一种在线控制框架，通过基于共识的保真度估计自适应选择物理映射来稳定噪声暴露，无需模拟器、离线训练或先前执行。在真实量子设备上，BAHAMAS可提升优化可靠性，还能在漂移下通过稳健的逐迭代控制支持推理时重定向。
英文摘要
Variational quantum algorithms (VQAs) suffer from unstable optimization due to temporal noise drift and static qubit mappings that distort gradient signals across iterations. We present BAHAMAS, an online control framework that stabilizes noise exposure by adaptively selecting physical mappings via consensus-based fidelity estimation, without requiring simulators, offline training, or prior executions. Across real quantum devices, BAHAMAS improves optimization reliability and supports inference-time retargeting under drift through robust, per-iteration control.
CommentsThis work is accepted for publication at the ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (Supercomputing, SC 2026)
