MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline

arXiv:2609.30104 · quant-ph, cs.DC, cs.LG, cs.PL · Submitted 2026-09-24 · Read on arXiv

quant-ph, cs.DC, cs.LG, cs.PL

Submitted: 2026-09-24

Updated: 2026-09-30

Terminology

• Actor — 38× in this paper · explained in 3 episodes
• Fidelity — 24× in this paper · explained in 2 episodes
• Quantum Computing — 13× in this paper
• Reinforcement Learning — 8× in this paper · explained in 4 episodes
• Reward Function — 7× in this paper · explained in 2 episodes
• Calibration — 6× in this paper · explained in 7 episodes
• Machine Learning — 4× in this paper
• High Performance Computing — 3× in this paper
• Intermediate Representation — 3× in this paper
• Benchmarking — 2× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Munich Quantum Valley — 2× in this paper
• Physical Constraints — 2× in this paper · explained in 7 episodes
• Random Forest — 2× in this paper
• Abstraction — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Argonne National Laboratory — 1× in this paper
• Benchmark — 1× in this paper · explained in 2 episodes
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Design Space Exploration — 1× in this paper
• Dual-Annealed Exploration Priming — 1× in this paper
• General Advantage Estimation — 1× in this paper
• Multi-Layer Perceptrons — 1× in this paper
• Multi-Level Intermediate Representation — 1× in this paper
• Multi-objective Optimization — 1× in this paper · explained in 4 episodes
• Munich Quantum Toolkit — 1× in this paper
• Noisy Intermediate-Scale Quantum — 1× in this paper
• Quantum Circuits — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 24 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Support Vector Machine — 1× in this paper
• Temporal Difference — 1× in this paper
• Verification — 1× in this paper · explained in 2 episodes

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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