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UID:submissions.supercomputing.org_SC25_sess211_misc222@linklings.com
SUMMARY:Bridging Realities: CUDA-Q's MLIR Pipeline for Quantum-Classical H
 ybrid Computing
DESCRIPTION:Bruno Schmitt (NVIDIA)\n\nThe integration of quantum processin
 g units (QPUs) with high-performance computing (HPC) infrastructures repre
 sents one of the most significant challenges in quantum computing today. A
 s quantum systems scale beyond the NISQ era toward utility-scale applicati
 ons, the need for robust, performance-optimized compilation toolchains bec
 omes critical for realizing quantum advantage in real-world scientific com
 puting workflows.​<br>Quantum compilation fundamentally differs from class
 ical compilation in ways that challenge traditional compiler design princi
 ples. While classical compilers optimize for performance metrics like inst
 ruction throughput and cache locality, quantum compilers must navigate the
  fragile nature of quantum superposition, where measurement destroys quant
 um states and decoherence imposes strict timing constraints. Quantum progr
 ams exhibit unique characteristics: some of their pieces (the quantum circ
 uits) are inherently reversible, programs operate on exponentially large H
 ilbert spaces and require hardware-specific gate decompositions that vary 
 dramatically across QPU architectures.<br>This talk presents some of the c
 hallenges in quantum compilation and CUDA-Q's approach to quantum-classica
 l hybrid compilation through its sophisticated MLIR-based compiler infrast
 ructure that seamlessly integrates quantum kernels with HPC environments. 
 We will talk about how CUDA-Q leverages the Multi-Level Intermediate Repre
 sentation (MLIR) framework to enable progressive lowering from high-level 
 C++ and Python quantum programs through multiple abstraction layers.\n\nRe
 cording: Livestreamed, Recorded\n\nRegistration Category: Technical Progra
 m Reg Pass, Workshop Reg Pass\n\nSession Chairs: James C. Brodman (Intel C
 orporation); Johannes de Fine Licht (NextSilicon Inc, ETH Zürich); Ryan Ka
 brick (Tactical Computing Laboratories LLC, University of Delaware); and A
 lexis Perry-Holby (Los Alamos National Laboratory (LANL))\n\n
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