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arXiv:2205.15256

Towards near-term quantum simulation of materials

Laura Clinton, Toby S. Cubitt, Brian Flynn, Filippo Maria Gambetta, Joel Klassen, Ashley Montanaro, Stephen Piddock, Raul A. Santos, Evan Sheridan

Published May 30, 2022• 8 citations
arXivHTMLPDF

Abstract

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.

DOI
10.48550/arxiv.2205.15256

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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.
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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 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.