@inproceedings{fe11fd56cd9c4d1db29ef60d3539d5d8,
title = "Performance Characterization of Quantum Simulation in CUDA-Q",
abstract = "The ongoing rise of interest in quantum computing (QC) is fueling considerable advances in quantum algorithms, as well as work on accelerating their simulation before sufficiently-capable QC prototypes are available. Since classical state-vector simulation time of QC systems scales exponentially with the number of qubits, QC simulator performance plays a significant role in algorithm and systems development. This paper provides a systems-level performance characterization of NVIDIA's CUDA Quantum (CUDA-Q) package, across several key metrics, on multiple GPU types. Our goal is to identify patterns or limitations in the implementation that may suggest future optimization opportunities. Our characterization also helps users on mid-scale GPU clusters plan likely runtime trajectories with problem size.11Professor Martonosi's research is supported in part by the US Department of Energy and the National Science Foundation.",
keywords = "Benchmarks, CUDA-Q, Quantum computing, Quantum Simulation Packages, Qubits",
author = "Ella Rubinshtein and Jocelyn Li and Margaret Martonosi",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 6th IEEE International Conference on Quantum Computing and Engineering, QCE 2025 ; Conference date: 31-08-2025 Through 05-09-2025",
year = "2025",
doi = "10.1109/QCE65121.2025.10463",
language = "English (US)",
series = "Proceedings - IEEE Quantum Week 2025, QCE 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "596--597",
editor = "Candace Culhane and Greg Byrd and Hausi Muller and Andrea Delgado and Stephan Eidenbenz",
booktitle = "Keynotes, Workshops, Posters, Panels, and Tutorials Program",
address = "United States",
}