MQSS-Selector：用强化学习统一量子编译器 pass 选择
MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline
量子计算编译圈的朋友可以看看：这篇用强化学习把 pass 选择、设备选择和调度捏到一个框架里，还兼顾保真度和延迟。
arXiv 论文提出 MQSS-Selector，一个基于强化学习和深度学习的编译 pass 选择器，面向 MLIR 编译流水线。它把设备选择、编译优化、任务调度三个原本分散的阶段整合进统一框架。方案可同时优化保真度、编译时间、调度延迟等多个目标，并根据电路特征和设备状态动态调整，应对 NISQ 设备易出错、资源受限的问题。
MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline
High Performance Computing (HPC) and Quantum Computing (QC) systems are increasingly converging towards unified High Performance Computing-Quantum Computing (HPCQC) infrastructures, driven by a growing need to bridge classical and quantum workflows, which affects all levels of the system stack, from the hardware to compilers and runtimes, all the way to applications. However, today's QC devices are still in the Noisy Intermediate-Scale Quantum (NISQ) era, are error-prone and resource-limited, and therefore require specialized optimizations and topology mappings to achieve sufficient fidelity. This places special emphasis on proper compilation and optimization within the overall quantum software stack. Many existing stacks remain fragmented, with separate components responsible for device selection, compiler-pass optimization, and job queue scheduling. This paper proposes a unified, learning-based selector that integrates these disparate stages into a cohesive framework. Our proposed selector scheme leverages reinforcement learning and deep learning models that can be extended to simultaneously optimize multiple objectives -- such as fidelity, compilation time, and scheduling latency -- while dynamically adapting to circuit characteristics and device conditions.
