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arXiv:2303.16144

An association sequence suitable for producing ground-state RbCs molecules in optical lattices

Arpita Das, Philip D. Gregory, T. Takekoshi, Luke Fernley, Manuele Landini, Jeremy M. Hutson, Simon L. Cornish, Hanns‐Christoph Nägerl

Published Jan 1, 2023• 0 citations
arXivHTMLPDF

Abstract

The data that support the findings of this study are uploaded here. All the data files are self-explanatory. They contain individual column names. The experimental data for Fig. 4(a) are in Fig4a_experimental_data.csv under the folders Fig_4>Fig_4a. To convert to binding energy there is a fitting algorithm in the Python code Feshbach_Fit.py .

DOI
10.48550/arxiv.2303.16144

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# 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. 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•Nov 28, 2025•1 citation
The data that support the findings of this study are uploaded here. All the data files are self-explanatory. They contain individual column names. The experimental data for Fig. 4(a) are in Fig4a_experimental_data.csv under the folders Fig_4>Fig_4a. To convert to binding energy there is a fitting algorithm in the Python code Feshbach_Fit.py .
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.
Description This repository contains the open access version of the manuscript Reliable quantum advantage in quantum battery charging, which can also be found on ArXiv. The manuscript has been published on Physical Review A; therefore, the published version is not open access, but the APS Transfer of Copyright Agreement applies. The published version can be found here. Moreover, the repository contains the Python code that has been exploited to perform the simulations discussed in the manuscript. Reference paper Published version: Phys. Rev. A 112, 012205 DOI: https://doi.org/10.1103/6kwv-z6fx Open access version on ArXiv: arXiv:2412.15339 DOI: https://doi.org/10.48550/arXiv.2412.15339 Python code Usage: Computation of the SNR associated with U, Q and W: Run SNR.py: it outputs data file Run SNR_plot.py: it plots the data (Figure 2 in the paper) Computation of the produced power and of the fidelity: Run charge.py: it outputs data file Run charge_plot.py: it plots the data (Figure 3.a and 3.b in the paper) Required libraries: - numpy - matplotlib.pyplot - matplotlib - os - met_brewer Important: the following scripts, - JCGEN.py - JC_evolution.py - veclib.py - operators_toolbox.py - distanceLib.py must be in the same folder of SNR.py, SNR_plot.py, charge.py, and charge_plot.py, since the latter call functions contained in the previous ones.
# 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