LAMINA: An MLIR-Based Translation Library for Heterogeneous Quantum-Classical Compilation
Broughton, Michael, Verdon, Guillaume, McCourt, Trevor, Martinez, Antonio J., Yoo, Jae Hyeon, Isakov, Sergei V., Philip Massey, Ramin Halavati, Murphy Yuezhen Niu, Alexander Zlokapa, Evan Peters, Owen Lockwood, Andrea Skolik, Sofiène Jerbi, Vedran Dunjko, Martin Leib, Michael Streif, David Von Dollen, Hongxiang Chen, Shuxiang Cao · arXiv (Cornell University) · 2020
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.