Randomized and Diverse Input State Generation for Quantum Program Testing

Maryse Ernzer

Published Jul 15, 2026• 0 citations

Abstract

This repository accompanies the articleMaryse Ernzer, Seung Yeob Shin, Fabrizio Pastore, Domenico Bianculli. Randomized and Diverse Input State Generation for Quantum Program Testing. IEEE International Conference on Quantum Computing and Engineering (QCE26). 2026. The repository contains:The scripts required to reproduce the data and figures as reported in the Evaluation section IV of the article.The datasets used in our article. These are found in the directory data_sets.

DOI: 10.6084/m9.figshare.32105962

Related papers

Works on My QPU: Reproducibility in Quantum Computing Research

Dominik Köster, Maja Franz, Benjamin Zec +3 more

This repository contains all scripts, data, and plotting code to reproduce the experiments and figures presented in the paper: Works on My QPU: Reproducibility in Quantum Computing Research (QCE 2026)

Jul 24, 20260 citations

Claim against Measurement: Statistical Artefacts in Quantum Error Mitigation Benchmarks

Dominik Köster, Wolfgang Mauerer

This repository contains all scripts, data, and plotting code to reproduce the experiments and figures presented in the paper: Claim against Measurement: Statistical Artefacts in Quantum Error Mitigation Benchmarks (QCE 2026)

Jul 24, 20260 citations

Engineered non-Gaussian Coherence as a Thermodynamic Resource for Quantum Batteries

Kingshuk Adhikary

This repository contains the numerical data and computational codes supporting the results presented in the manuscript: "Engineered non-Gaussian Coherence as a Thermodynamic Resource for Quantum Batteries". The archive (data_QB.zip) includes all datasets, Jupyter notebooks, and figure files required to reproduce the results reported in both the main text and the Supplemental Material. Each figure is organised in a separate directory containing the corresponding data (.txt), code (.ipynb), and generated plots (.png/.pdf). The provided materials are sufficient to reproduce all figures and results without restriction. Detailed instructions and directory structure are described in the accompanying README.txt file. This dataset is made openly available to ensure transparency and reproducibility of the reported results.

Apr 11, 20260 citations

Automatic Generation of Testing Circuits for Deterministic Quantum Algorithms

Antonio García de la Barrera, Manuel Serrano, Ignacio García‐Rodríguez de Guzmán +2 more

Jan 1, 20231 citation

Controlling emergent dynamical behavior via phase-engineered strong symmetries

Marc Nairn

This record contains code, notebooks, data, and figure scripts associated with the project. The description below lists the contents of the upload and their roles. strongsym.zip: Zip archive used for the Zenodo upload. It contains the simul/, finsize_data/, and dependencies/ directories. simul/ : Simulation and analysis notebooks and scripts. MF_*.ipynb - Mean-field analysis notebooks. finite_size_spins.ipynb - Finite-size dynamics for 2 level system alt_finite_size_spins.ipynb - Alternative finite-size with a stress on Liouvullian spectrum analysis in the two level system. liouv_spec_spin1.ipynb -Finite-size spectrum analysis for three level model. find_symmetry.ipynb - Symbolic symmetry-finding notebook. finsize_data/ : Data files used to generate figures. fig_script.ipynb: Notebook to generate the figures overview_sketch.ipynb: Notebook to generate schmatics and diagrams (no raw data) dependencies/ : Environment specification files, for both python (.yml) and julia (.toml) Usage: Unzip strongsym.zip, set up environments using the files in dependencies/, and run the notebooks in simul/. Data required for figures is in finsize_data/; figure scripts are at the repository root. For a more detailed walkthrough and documentation, see the GitHub repository: https://github.com/MarcNairn/spin_strong_symmetries_phase

Feb 1, 20261 citation

Numerical Notebook and Data for "Eigenbasis Conditioning and Fisher-Information Singularity Classes Near a Second-Order Exceptional Point"

Markus, Olga

This repository contains the complete Python notebook and numerically generated data supporting Sections 7.1–7.5 and Figs. 1–3 of the accompanying article, “Eigenbasis Conditioning and Fisher-Information Singularity Classes Near a Second-Order Exceptional Point.” The archive includes the exact canonical EP2 benchmark, a reproducible ensemble of 300 noncanonical continuous complex operator paths, the Bernoulli Fisher-information readout models, the relative Fisher–conditioning analysis, and the numerical precision, unitary-invariance, and nonunitary-similarity tests. The repository contains the complete ensemble-generation parameters, recorded pseudorandom seeds, processed numerical data, software-environment specifications, and files underlying the reported figures. All data in the archive are numerically generated. The notebook code is released under the MIT License. The generated numerical data, figures, and documentation are released under the Creative Commons Attribution 4.0 International License.

Jul 17, 20260 citations