MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline
quant-ph, cs.DC, cs.LG, cs.PL
Submitted: 2026-09-24
Updated: 2026-09-30
Comments: 11 pages, 5 figures, 1 table
License: http://creativecommons.org/licenses/by/4.0/
The gist: High Performance Computing (HPC) and Quantum Computing (QC) systems are increasingly converging towards unified High Performance Computing-Quantum Computing (HPCQC) infrastructures, driven by a
Terminology
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.
Sources
- Geometric approach to quantum statistical inference
- MLIR: A Compiler Infrastructure for the End of Moore's Law
- Beyond the Phase Ordering Problem: Finding the Globally Optimal Code w.r.t. Optimization Phases
- The Easiest Hard Problem: Number Partitioning
- Reinforcement Learning for Quantum Technology
- MLGO: a Machine Learning Guided Compiler Optimizations Framework
- AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement Learning
- Quantum resources in resource management systems
- Quantum circuit optimization with deep reinforcement learning
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