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 repository provides the figure data and numerical simulation codes supplement to the manuscript “Dynamical freezing and enhanced magnetometry in an interacting spin ensemble” (arXiv:2507.22982). Contents:--Main_text_figures--Extended_Data_figures Each folder contains the numerical or experimental data used to generate the corresponding figures in the manuscript. All figures can be reproduced by straightforward plotting of the included .csv files. A short description of each dataset is provided within the file headers. --Exact_diagonalization_code--Discrete_truncated_Wigner_approximation_code These directories contain the codes used in the numerical simulations presented in the paper. Each code package includes a detailed README file explaining the workflow, parameters, and usage.
# Sensing with discrete time crystals Data Repository This repository contains experimental data, simulation results, and analysis code for Discrete Time Crystal (DTC) sensing research. The data is organized by figure numbers corresponding to the main manuscript and supplementary materials. ## Repository Structure ``` DTC_sensing_Zenodo_Repo/ │ ├── data/ # All experimental and processed data │ ├── raw/ # Raw experimental data files │ │ ├── fig-1/ # Figure 1 raw data │ │ ├── fig-2/ # Figure 2 raw data (fig-2b, fig-2c) │ │ ├── fig-3/ # Figure 3 raw data (fig-3a, fig-3b, fig-3c) │ │ ├── fig-4/ # Figure 4 raw data │ │ └── fig-S1/ through fig-S18/ # Supplementary figures raw data │ │ │ └── processed/ # Processed and analyzed data │ ├── fig-3/ # Processed data for Figure 3 │ └── fig-S14/ # Processed data for Figure S14 │ ├── src_code/ # Source code for analysis and simulations │ ├── simulations/ # Simulation code │ │ ├── SingleToneDTC/ # Single-tone DTC simulations │ │ ├── TwoToneDTC/ # Two-tone DTC simulations │ │ ├── helper.py # Helper functions │ │ ├── setup_system.py # System setup utilities │ │ └── systems.yaml # System configuration parameters │ │ │ └── utils/ # Utility functions │ ├── plotting_utils.py # Plotting and visualization utilities │ └── simulation_utils.py # Simulation utilities │ └── visual_elements/ # Code and notebooks for figure generation ├── main/ # Main manuscript figures │ ├── fig-1/ # Figure 1 generation code │ ├── fig-2/ # Figure 2 generation code │ ├── fig-3/ # Figure 3 generation code │ └── fig-4/ # Figure 4 generation code │ └── supmat/ # Supplementary material figures └── fig-S1/ through fig-S18/ # Supplementary figure generation code ``` ## Data Description ### Raw Data (`data/raw/`) The raw data directory contains experimental measurements organized by figure number: - **File Formats:** - `.mat` files: MATLAB data files containing experimental measurements from the Proteus P9484M NMR spectrometer - `.csv` files: CSV files containing contour and magnetometer data (fig-S1) - `.hdf5` files: HDF5 files containing simulation results (fig-S13-S16) - `.npy` files: NumPy array files - **Data Organization:** - **Main Figures (fig-1 through fig-4):** Core experimental results - **Supplementary Figures (fig-S1 through fig-S18):** Additional experimental data, simulations, and analysis ### Processed Data (`data/processed/`) Processed data contains analyzed and pre-computed results: - **File Formats:** - `.npy` files: NumPy arrays containing processed experimental data - **Available Processed Data:** - `fig-3/`: Processed data for Figure 3 (fig-3a, fig-3b) - `fig-S14/`: Processed data for Figure S14 (AC phase scans, frequency scans, Vpp scans) ## Source Code ### Simulation Code (`src_code/simulations/`) - **SingleToneDTC/**: Code for single-tone DTC simulations - **TwoToneDTC/**: Code for two-tone DTC simulations - **systems.yaml**: Configuration file defining system parameters (N, rmin, rmax, gamma, theta, tau, etc.) - **helper.py**: Helper functions for simulations - **setup_system.py**: System setup and initialization utilities ### Utility Functions (`src_code/utils/`) - **plotting_utils.py**: Functions for data visualization, including matplotlib configuration and plotting routines - **simulation_utils.py**: Utilities for running and analyzing simulations ## Figure Generation ### Visualization Code (`visual_elements/`) Each figure directory contains: - **Jupyter notebooks** (`.ipynb`): Interactive notebooks for data analysis and figure generation OR - **Python scripts** (`.py`): Standalone scripts for figure generation - **Output files** (`.pdf`, `.svg`): Generated figure files ## Requirements All required Python packages are listed in `requirements.txt`. Key dependencies include: - **NumPy** (2.3.5): Numerical computations - **SciPy** (1.16.3): Scientific computing and data analysis - **Matplotlib** (3.10.7): Plotting and visualization - **h5py** (3.15.1): HDF5 file handling - **Pandas** (2.3.3): Data manipulation - **Jupyter/IPython**: Interactive notebooks - **PyYAML** (6.0.3): Configuration file parsing To install dependencies: ```bash pip install -r requirements.txt ``` ## Data Access ### Reading Raw Data Raw experimental data is stored in MATLAB `.mat` format. To read these files in Python: ```python from scipy.io import loadmat data_dict = loadmat('path/to/file.mat') ``` ## Usage 1. **Install dependencies**: `pip install -r requirements.txt` 2. **Navigate to figure directory**: Each figure has its own directory in `visual_elements/` 3. **Run analysis**: Open the Jupyter notebook (`.ipynb`) or Python script (`.py`) for the desired figure 4. **Data paths**: The code assumes data is in the `data/` directory relative to the repository root ## Notes - Raw data files are named with timestamps (e.g., `2024-06-06-125713_Proteus.mat`) indicating when measurements were taken - Some figures contain subdirectories (e.g., `fig-2b/`, `fig-2c/`) for different panels - The repository structure follows the organization of figures in the associated publication
Data from the simulations presented in arXiv:2104.00569, and generated with the code published in https://github.com/matteoacrossi/adapt_ic-povm # Instructions The file data.tar contains the following files: hamiltonians_h_chain.pickle hamiltonians_params.json hamiltonians.pickle chemistry.feather h_chain.feather tomography_counts_data.jsonl fidelities_tomo.json The files `hamiltonians*` contain the Hamiltonians considered in the paper, as well as the corresponding exact ground states and example VQE ground states. The files `chemistry.feather` and `h_chain.feather` contain all the results for the molecules and hydrogen chain (respectively). The files `tomography_counts_data.jsonl` and `fidelities_tomo.json` contain (respectively) the raw data for tomographical reconstruction and the fidelities of the reconstructed density matrices used to produce Fig. 5. The files can be processed using the Figures.ipynb file in the linked repository. Please refer to the README.md file for more detail on how to use and reproduce this data
Overview of data provided in support of Towards near-term quantum simulation of materials Contents of this folder: `analyse_materials_results.py`: script used to generate the summary tables and figures presented in the manuscript. `towards_quantum_simulation_data`: raw data used and produced when studying various 3D materials. `towards_quantum_simulation_analysis`: tables (in `.tex` format) and figures (as PDFs) presented in the manuscript. The contents of `towards_quantum_simulation_analysis` were generated by `analyse_materials_results.py`, i.e. the user need not run the script to produce the output files. Users can generate these analyses directly by running ```python analyse_materials_results.py```, though note the following packages will first need to be installed: `pandas >= 1.2.3` `numpy >= 1.23` `seaborn >= 0.11.1` `lfig >= 0.1.3` `matplotlib > 3.7.0` Included data Within `towards_quantum_simulation_data`, there are subfolders for each of the materials described in the manuscript, i.e. `SrVO3`, `GaAs`, `H3S`, `Si`, `Li2CuO2`. Within each material's folder are further subfolders for the `hamiltonian` and `encoding` used to represent the material, as well as subfolders for each of the `algorithms` studied. `hamiltonian`: files which specify the Hamiltonian for the material under study. There are a number of files `interactions.json` Hamiltonian terms in terms of Majorana monomials. `map_majorana_to_mode.json` keys are Majorana indices; values are the mode index to which they are associated. `map_mode_to_group.json` keys are mode indices; values are the group (or site) index to which they are associated. `map_group_to_position.json` keys are group indices; values are the corresponding 3D Cartesian coordinates of the lattice used to represent the material. `stage_data.json` contains key/value pairs of any other fields of interest. `encoding` files which specify the fermionic encoding which is customised for the material under study. `encoding.json` which details the edges of the hybrid compact encoding described in Section VI of the supplementary material. `precompiler.json` contains all the information which permits the encoding construction, including the Hamiltonian terms (`_interactions`) which match those in `hamiltonian/interactions.json`. The same mappings as present in the Hamiltonian data(a.g. `map_group_to_position`). `stage_data.json` contains key/value pairs of any other fields of interest. `algorithms` contains subfolders for each of the algorithms desribed in the manuscript Those explored for circuits depths: `TDSSplitTermsPriorityCircuitDepth` (TDS in the manuscript) `TDSSplitTermsPriorityCircuitDepthNoSwapNetwork` (TDS\*) `VQESplitTerms` (VQE) `VQESplitTermsNoSwapNetwork` (VQE*) each of which contain the files, inside the `circuitry` folder: `circuit_terms.csv`, which lists each individual term, together with their Pauli string and rotation angle, required to construct the corresponding quantum circuit `circuit_layers_to_implement.csv` groups the same terms into layers to achieve parallelism in the circuit. `stage_data.json` contains key/value pairs of any other fields of interest. and those used to compose measurement layers, as outlined in Section VII D of the supplementary material: `MeasurementCommutativity` `MeasurementNaiveQubitwise` `MeasurementNonCrossing` each of which contain the files, inside the `compilation` folder: `layers.csv` lists the terms which may be measured simultaneously to achieve the measurement strategies shown in Table S14. `stage_data.json` contains key/value pairs of any other fields of interest. CSV files In `towards_quantum_simulation_data`, there are unified CSV files containing the results of applying the procedures described in the manuscript to the target materials. `circuit_costs.csv`: results of running the circuit compiler described in the manuscript. `measurements.csv`: results of running the measurement compiler described in the manuscript. These CSVs are used in the analysis script `analyse_materials_results.py` to produce the figures and tables presented in the manuscript.
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`.