LAMINA: An MLIR-Based Translation Library for Heterogeneous Quantum-Classical Compilation
Bibliographic record
Abstract
Quantum computing is increasingly integrated into High-Performance Computing (HPC) environments, where quantum processors act as specialized accelerators within hybrid workflows. The Munich Quantum Software Stack (MQSS) - a unified compilation and runtime framework for hybrid quantum–classical computing - provides the foundation for this integration. However, the growing heterogeneity of applications demands more flexible compilation tools. This work introduces an MultiLevel Intermediate Representation (MLIR)-based translation library that extends MQSS by enabling the conversion of CUDA-Quantum (CUDA-Q) (quake) dialects into machine learning–oriented MLIR representations compatible with modern compiler ecosystems. Leveraging MLIR’s dialect-driven design, the library enables hardware-agnostic transformations, device-specific optimizations, and seamless integration with MQSS components. The proposed approach bridges quantum compilation and contemporary machine learning frameworks, facilitating GPU-accelerated circuit simulation, hybrid quantum–classical workflows, and heterogeneous execution, thereby advancing a unified compiler infrastructure for quantum computing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.016 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".