This data repository contains the information necessary to reproduce the main results of the paper “Quantum computational displacement sensing”. The repository contains both the data required to generate the figures of the main manuscript and the supplemental material, as well as source code for running the experiments and simulations presented in the paper. There are two main folders: code_simulation (which consists of the training code and the baseline simulations) and code_experiment (which covers the experimental code). The corresponding requirements for running them are in the zipped folder. The data folder contains all simulation and experimental raw and processed data generated. The figures folder contains all the plots presented in the manuscript.
This data repository contains the information necessary to reproduce the main results of the paper “Exponential quantum advantage for learning signals with a single qubit”. The repository contains both the data required to generate the figures of the main manuscript and the supplemental material, as well as source code for running the experiments and simulations presented in the paper. There are two main folders: experiment_code (which contains all experimental scripts to calibrate device and run the protocols presented in the paper) and simulation_code (which contains simulation details of the dark matter and communication detection tasks) The data folder contains experimental raw and processed data generated. The figures folder contains all the plots presented in the manuscript.
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.
## Repository of raw data and code for the paper: [Topological devil's staircase in a constrained kagome Ising antiferromagnet](https://doi.org/10.1103/dgyr-5y75)1. The CTMRG simulation data is included in `CTMRG_Data`. In order to open each file, use the [JLD2](https://github.com/JuliaIO/JLD2.jl) library. A minimum working example is```using JLD2file=data=load_object(file)```2. Snapshots saved, also as JLD2 files, in the folder `Snapshots`. Each snapshot is stored as a
Lx×Ly matrix of integers
ci,j∈{1,⋯,2N}, where each integer defines the configuration of a 12-spin cluster. For more details, see the section S1 of the Supplementary Information.3. For running the code, run `julia --project=ctmrg` in folder `Code` and then run `include(".jl")`.
This repository contains the Julia source code used to generate the numerical data and transmission probability maps presented in **Figure 5** of the manuscript: "Floquet Engineering of Quantum Transport through two Driven Impurities" by V. Bruno, C. Kollath, R. Citro, and A. Sheikhan. ### OverviewThe code implements the boundary conditions for a driven quantum system to determine the probability amplitudes across multiple Floquet channels (specifically 41 channels in the default configuration). The core function, `calculate_transmission_continuum`, sets up and solves the linear system
A⋅b to compute the total transmission probability as a function of the scattering energy (
E) and the driving amplitude (
γ). This function serves as the computational backbone for most of the numerical results presented in the paper. The script uses parallel multithreading to efficiently sample a
500×500 grid of parameters, generating two distinct datasets for the transmission maps:* **Map 1:** Transmission probability matrix
T1 for a potential barrier strength
ν=0.0.* **Map 2:** Transmission probability matrix
T2 for a potential barrier strength
ν=0.7. ### Repository Contents* `transmission_fig5_generation.jl`: The complete Julia script containing the solver logic, the multithreaded data generation routine (`genera_dati`), and the physical parameters chosen for the publication. ### Prerequisites & DependenciesTo run this code, you need a working installation of the Julia programming language along with the following packages:* `PyPlot` (for visualization alignment)* `LinearAlgebra` (for matrix inversion via the `\` operator)* `ProgressMeter` (to monitor the multi-threaded grid computation)* `LaTeXStrings` ### How to Run1. Enable multithreading in your Julia session (e.g., start Julia with `julia -t auto` or set the `JULIA_NUM_THREADS` environment variable).2. Execute the script to generate the variables `E_dati`, `gamma_dati`, `T_map1`, and `T_map2`.