MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline
Abstract
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.
Related
- Plateau-Constrained Selection: Exploiting Degeneracy for Lower-Depth Quantum Compilation
- DANTE: A Reference-Guided Unsupervised Pipeline for Extended-Transient Anomaly Characterization in LIGO O4a
- Beyond Binary Rewrite Decisions: Separating Rewrite-Intensity Control from Level Prediction in a Frozen-LLM Generation-and-Selection Pipeline for Korean Easy-Read Drafting
- Physics-Guided Source Selection for Few-Shot Transfer Learning to Characterise Bismuth-Doped Fiber Amplifier
- Towards Natural Gas Contract Selection via Quantum-Guided Independent Set Reduction
BibTeX
@misc{youssefi2026mqssselector,
title = {{MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline}},
author = {Andre Youssefi and Ercüment Kaya and Minh Chung and Jorge Echavarria and Laura B. Schulz and Martin Schulz},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.30104},
eprint = {2609.30104},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.30104},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline
AU - Andre Youssefi
AU - Ercüment Kaya
AU - Minh Chung
AU - Jorge Echavarria
AU - Laura B. Schulz
AU - Martin Schulz
PY - 2026
DA - 2026/09/30
PB - arXiv
DO - 10.48550/arxiv.2609.30104
UR - https://arxiv.org/abs/2609.30104
AB - 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.
ER -