Data and code to reproduce the "Freezing and Shielding under Global Quenches for Many-Body Systems with Long-Range Couplings" data in https://arxiv.org/abs/2407.06072. The mathematica file just perform a numerical integral that computes the double occupaciens with second order unitary perturbation theory for a semi-circular density of states of the unperturbed state. The python files also compute double occupaciens with second order unitary perturbation theory for a finite system where the density of states cannot be approximated by a continuous functions but it is just a collection of eigenenergies. The Fig1.ipynb file computes the dynamics of three local excitations for the spin model and the Fermi-Hubbard model with off-diagonal disorder and long-range hopping/interactions. Ther is a python file that computes the fidelity decay.
Data and code to reproduce the "Freezing and Shielding under Global Quenches for Many-Body Systems with Long-Range Couplings" data in https://arxiv.org/abs/2407.06072. The mathematica file just perform a numerical integral that computes the double occupaciens with second order unitary perturbation theory for a semi-circular density of states of the unperturbed state. The python files also compute double occupaciens with second order unitary perturbation theory for a finite system where the density of states cannot be approximated by a continuous functions but it is just a collection of eigenenergies. The Fig1.ipynb file computes the dynamics of three local excitations for the spin model and the Fermi-Hubbard model with off-diagonal disorder and long-range hopping/interactions. Ther is a python file that computes the fidelity decay.
Shortcuts to adiabaticity with a quantum control field — Code & data========================================================== This repository contains a Jupyter notebook and supporting data files (csv) to reproduce the numerical results and figures in our manuscript (arXiv 2508.06304), which investigates adiabatic shortcuts mediated by a quantum control field.The code implements a Landau–Zener two-level system coupled to a spectator qubit, time evolution of the full system (LZ qubit + spectator) via QuTiP, parameter sweeps (parallelized with joblib), and plotting utilities for the manuscript figures. Related publication----------------------------This code is complementary to the preprint: https://arxiv.org/abs/2508.06304. Requirements---------------------The simulations were developed for and tested on Python 3.9+. Recommended packages and minimum versions: - Python >= 3.9- numpy >= 1.22- scipy >= 1.8- matplotlib >= 3.5- qutip >= 4.6 (install a recent stable qutip; some installations require `pip install qutip` or `conda` channels)- joblib >= 1.2- tqdm >= 4.60- pandas (optional, for CSV handling)- jupyterlab / notebook (for interactive use) Running the notebook (interactive) Open the notebook with JupyterLab or Jupyter Notebook. Execute cells sequentially in the order indicated by the notebook outline.The sections “Definitions” and “Figure imports and setup” must be executed before generating any data or figures, as they define all operators, and helper functions. The notebook is organized into separate sections for each figure of the manuscript, allowing you to re-run only the figure(s) of interest. Note: Figure 4 (Density plot) is configured to load precomputed data files to reduce computational cost. To reproduce Figure 4 without recomputing the full dataset, ensure the required .csv files are downloaded and located in the same directory as the notebook. To explore different parameter regimes: Modify the relevant parameter definitions (e.g., anneal_window, g, epsilon, omega_c, x0, x0_vals, q_vals). To include dissipation of the spectator (ancilla) qubit, set the parameter kappa> 0. For questions about reproducing the figures or about parameter choices used in the paper, contact the corresponding authors listed in the preprint. If you reuse this data/code, please cite the associated preprint and this code archive.
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.
We provide the numerical codes to produce the data contained in "Competition between pair and single-particle superfluidity in bosonic quasi-flat bands: A Gaussian state approach", https://arxiv.org/abs/2605.26231, as well as notebooks to plot them. v1 contains a minimal version of Python and Julia codes. v2 provides the final version of the plots, explicitly stores data used in the plots and avoids Julia notebooks (in order to avoid compatibility issues). The LaTeX script allows to generate the manuscipt. Some of the Python codes rely on the external library QuSpin.py.
These files serve to comply to the data availability statement for the publicpation of (https://arxiv.org/abs/2509.14406) to PRE. The 'Local Minima' python notebook is the code used to (after choosing/generating the desired spectrum) compute the SFF and the corresponding Y variable defined in Eq. (16) of the text, from which its empirical distribution shown or various cases (Figures 1,2,6,7,10,11 and A3 in the text) is readily obtained. Finally, the notebook also includes the computation of the local minima discussed in the beginning of Section V. The data can be then plotted as is done in Figures 8 and 12 in the text. The .npy files '3003_FORSFF.npy', '792_FORSFF.npy', etc. are examples of generated spectra for the Hermitian Model defined in Eq. (23) of the text. Each file is a NumPy array containing energy spectra for many independent disorder realizations of the model at fixed system size. The array has shape (n_samples, D), where n_samples is the number of realizations and D is the Hilbert-space dimension. Each row corresponds to one realization, and each column corresponds to an energy level; within each row, the eigenvalues are real and sorted in ascending order. From these spectra, the distribution of the local energy gap ratios defined in Eq. (19) can be empirically obtained as is shown in Fig. 4 of the text. Using these spectra, the Spectral Form Factor (Eq (1) and its quenched and annealed counterparts (Eq.s (7) and (8) respectively) are obtained. The corresponding data examples are given in 'D3003_sff_L_14_logtimes' and D792_sff_L_12_logtimes''. Each CSV file contains ensemble-averaged spectral form factor data computed from the corresponding eigenvalue file. Rows correspond to different time values, and columns store the time (time), the averaged spectral form factor (K), its disconnected part (K_disc), the connected contribution (K_conn), and the ensemble average of the logarithm of the spectral form factor (mean_lnSFF). The results for the quenched SFF are shown in Fig. 5 of the text The various DSFFs (Eq, (5)) for the Dissipative XY model defined in Eq. (25) are obtained using samples found in the file 'XY_spin_L_14_dim_3003_g_0.4.npy'. The file contains complex energy spectra of a bosonic lattice Hamiltonian for many independent disorder realizations at fixed model parameters. The stored array has shape (n_samples, dim), where n_samples is the number of disorder realizations and dim is the Hilbert-space dimension of the fixed-particle-number sector. Each row corresponds to one realization of the random onsite potential, and each column corresponds to one eigenvalue of the Hamiltonian; the eigenvalues are complex and are not sorted. With these, the empirical distributions of the Complex Spacing ratios (Figures 13 and 14) can be obtained. The DSFF data is saved in the file 'sff_data_XY_3003_g_0.4.npz'. It stores a one-dimensional array of time values (times), a one-dimensional array of projection angles (phis), and the Hilbert-space dimension (D). For each angle, the file includes the sample-averaged spectral form factor (mean_SFF_label), the sample-averaged complex partition sum (mean_Z_label), and the sample average of the logarithm of the spectral form factor (mean_log_SFF_label), all stored as two-dimensional arrays with rows corresponding to different angles and columns corresponding to different times. Similar Constructions are available for the standard RMT spectra and the Random Energy Model
Python notebooks reproducing the numerical results and figures of the manuscript "Qubit encodings in the p-orbital-valley spectrum for enhanced coherence and tunable two-qubit interaction" by John H. Caporaletti and J. P. Kestner (University of Maryland Baltimore County). The manuscript proposes the pOv qubit, an encoding in the two-level subspace spanned by the lowest p-orbital state in the excited valley and the excited p-orbital in the ground valley of an anisotropic silicon quantum dot, coupled by disorder-induced valley-orbit coupling (VOC). This archive contains three self-contained notebooks: VOC_std_calc.ipynb — evaluates the VOC statistics of Appendix VI A using the discretized effective-mass theory of alloy disorder: the vertical envelope function is obtained from a finite-difference solve of the heterostructure confinement potential, and the standard deviations of the disorder-induced generator coefficients, together with the mean valley splitting and mean VOC magnitude, follow from the lattice sum over independent cells. The correlation matrix of the VOC generators is also reported. fig6_Qeff_hist.ipynb — reproduces Fig. 6. For each of 104 alloy-disorder realizations it finds the pair of dot orientations that switches off the two-qubit coupling Omega_zz while maximizing the effective two-qubit quality factor Q_eff, and histograms the result. fig7_inf_scatter_plot.ipynb — reproduces Fig. 7. It synthesizes the R_ZZ(π/2) entangling gate for each disorder realization and evaluates its infidelity against a Monte-Carlo ensemble of quasistatic two-level-fluctuator dipole configurations, comparing a single-segment analytic pulse with a three-segment composite pulse obtained from a two-stage optimization (L-BFGS-B on the deterministic infidelity with analytic gradients, then SLSQP minimizing total gate time subject to a deterministic-infidelity budget). All notebooks use the device parameters of Table I, run top to bottom with no input files, and require only NumPy, SciPy, and Matplotlib (developed under Python 3.12). All random draws are seeded, so the published figures reproduce exactly.