
With this reproduction package the results published in the paper "Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models" by Maja Franz*, Melvin Strobl*, Leonid Chaichenets, Eileen Kuehn, Achim Streit, and Wolfgang Mauerer, submitted to the IEEE International Conference on Quantum Computing and Engineering (QCE25), 2025, can be reproduced. A preprint of the paper can be found on arXiv (We will update the link, once it is available) The source code and supplementary material is also available on GitHub. For information on how to replicate our results, we refer to the README.md (either in the artifact, or on GitHub). Supplementary results beyond the scope of the paper are also described in the documents of the artifact, or on this GitHub page. Artifacts: "effect-of-noise-in-qfms.tar.gz" -- compressed source code (same as on GitHub) "effect_of_noise_repro.tar" -- Prebuilt Docker image, load with "docker load -i effect_of_noise_repro.tar" "csv_data.tar.gz" -- compressed results data used for the plots in the paper. For a replication of our plots follow the following steps: Download and unpack the artifact (e.g. with "tar -czf "csv_data.tar.gz") Move the folder "csv_data" to "/plotting/rplots/csv_data", where the "" refers to the root directory of the Git repository (or the source code in general). (a) When using docker, the option "plot_paper_results" can be called (ensure that a Volume is set in "docker run" with "-v $PWD/plotting:/home/repro/effect-of-noise-in-qfms/plotting").(b) Alternatively, when not using Docker, the script "./scripts/run.sh plot_paper_results" is available. *equal contribution
Entanglement, Fourier Analysis, Expressibility, Quantum Machine Learning
Entanglement, Fourier Analysis, Expressibility, Quantum Machine Learning
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