UniQ

UniQ is a unified programming model for efficient quantum circuit simulation. It supports state vector simulation, density matrix simulation by gates, and density matrix simulation by Kraus operators on various hardware. UniQ can accelerate quantum circuit simulation by up to 28.59× (4.47× on average) over state-of-the-art frameworks, and successfully scale to 399,360 cores on 1,024 nodes.

Reproduce Our Results

We have provided dockers on CPU and GPU for reproducing our results. The docker images are available at https://doi.org/10.5281/zenodo.6628189. The full code for reproducing the experiments, including Dockerfile and scripts for running the baselines, are available at https://doi.org/10.5281/zenodo.6628201. Please find the guideline of using these scripts from the artifact appendix of our paper.

Get the Source Code

Use the following command to clone UniQ and its submodules:

git clone https://github.com/thu-pacman/UniQ.git --recursive

Files in tests/ are saved with git lfs. Please ensure these files are downloaded.

Usage of UniQ

UniQ needs to be recompiled (by ./compile.sh -Dxxxx=xxxx in artifact-evaluation folder) to support different simulation methods and hardware.

To configure UniQ to a specific simulation methods, please use -DMODE option. The available options are:

• statevec: state vector simulation
• densitypure: density matrix simulation by gates
• densityerr: density matrix simulation by Kraus operators

To configure UniQ to a specific hardware, please use -DHARDWARE option. The available options are:

• cpu
• gpu

For the full commands, please refer to artifact-evaluation folder. These scripts are used to generate the results in our paper.

• Fig.8: sv-strong-gpu.sh
• Fig.9: pure-strong-gpu.sh
• Fig.10: pure-strong-gpu-nvprof.sh
• Fig.11: sv-strong-cpu.sh
• Fig.12: err-strong-gpu.sh
• Fig.13 and Fig.14: sv-breakdown-cpu.sh
• Fig.16: dm-cpu.sh

Citation

If you find UniQ useful for your research, please cite our paper:

@inproceedings{zhang2022uniq,
  title={UniQ: A Unified Programming Model for Efficient Quantum Circuit Simulation},
  author={Zhang, Chen and Wang, Haojie and Ma, Zixuan and Xie, Lei and Song, Zeyu and Zhai, Jidong},
  booktitle={2022 SC22: International Conference for High Performance Computing, Networking, Storage and Analysis (SC)},
  pages={692--707},
  year={2022},
  organization={IEEE Computer Society}
}