QuantenSplit

Repository of Prototype for IEEE Services Submission Architectural Vision for Quantum Computing in the Edge-Cloud Continuum

Notes

• This is a simple extension of FrankenSplit to support Hybrid Classical-Quantum Predictors. I tried to remove all redundant references that were not necessary for the experiments in the paper, but I may have missed some things
• I may include a more convenient way to run all experiments, however, I don't plan on actively maintaing this repository/monitor issues. In case you need my assitance, or you notice some problems (e.g. missed reference, broken implementation) please contact me at: a.furutanpey@dsg.tuwien.ac.at
• You can easily change the configuration in the yaml file and implement your own Backbones, Compression Models and Ansatz composition

Preparation

1. Download and Prepare the ILSVRC2012 dataset from the official site (Apparently, I'm not allowed to include a direct download link)
2. Set the repository root as content root and run misc/create_subsets.py
3. Run train_compressor.py with --config config/compressor/FP-baseline_compressor-l032.yaml (See train_util for optional arguments)
4. Add as many seeds for the number of runs in train_predictors.sh
5. Run bash train_preidctors.sh cuda 8 HybridQNN alternating_rotation_circuit default.qubit (replace cuda with cpu if necessary)

Citation

Preprint Paper

@INPROCEEDINGS{10234305,

  author={Furutanpey, Alireza and Barzen, Johanna and Bechtold, Marvin and Dustdar, Schahram and Leymann, Frank and Raith, Philipp and Truger, Felix},

  booktitle={2023 IEEE International Conference on Quantum Software (QSW)}, 

  title={Architectural Vision for Quantum Computing in the Edge-Cloud Continuum}, 

  year={2023},

  volume={},

  number={},

  pages={88-103},

  doi={10.1109/QSW59989.2023.00021}}

References

• Furutanpey, Alireza, Philipp Raith, and Schahram Dustdar. "FrankenSplit: Saliency Guided Neural Feature Compression with Shallow Variational Bottleneck Injection." arXiv preprint arXiv:2302.10681 (2023).
• Bergholm, Ville, et al. "Pennylane: Automatic differentiation of hybrid quantum-classical computations." arXiv preprint arXiv:1811.04968 (2018).
• Mari, Andrea, et al. "Transfer learning in hybrid classical-quantum neural networks." Quantum 4 (2020): 340.
• Matsubara, Yoshitomo. "torchdistill: A modular, configuration-driven framework for knowledge distillation." Reproducible Research in Pattern Recognition: Third International Workshop, RRPR 2021, Virtual Event, January 11, 2021, Revised Selected Papers. Cham: Springer International Publishing, 2021.
• Wightman, Ross. "Pytorch image models." (2019).
• Bégaint, Jean, et al. "Compressai: a pytorch library and evaluation platform for end-to-end compression research." arXiv preprint arXiv:2011.03029 (2020).
• Ballé, Johannes, Valero Laparra, and Eero P. Simoncelli. "End-to-end optimized image compression." arXiv preprint arXiv:1611.01704 (2016).