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wsantmartins/2025RydPenning: Quantum simulation with Rydberg ions in a Penning trap

Wilson S. Martins

Published Dec 8, 2025• 0 citations
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Abstract

Quantum simulation with Rydberg ions in a Penning trap Authors: Wilson S. Martins, Markus Hennrich, Ferdinand Schmidt-Kaler, and Igor Lesanovsky This repository contains the numerical simulations used to generate the figures presented in the main text of the manuscript. All scripts are written in Python (.py) and generate output figures in the user's local directory. The most important folders in this repository are: https://github.com/matrices Contains the operators expressed in the fine-structure basis, truncated at orbital angular momentum l={S,P,…,H}. This includes, for example: The total Hamiltonian The dipole operator These matrices are used throughout the simulations. @figures Contains the post-processed figures in .pdf and .svg format used in the manuscript. The simulation scripts save pre-processed figures in this folder, which are subsequently edited for final presentation.

DOI
10.5281/zenodo.17857139

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Quantum simulation with Rydberg ions in a Penning trap Authors: Wilson S. Martins, Markus Hennrich, Ferdinand Schmidt-Kaler, and Igor Lesanovsky This repository contains the numerical simulations used to generate the figures presented in the main text of the manuscript. All scripts are written in Python (.py) and generate output figures in the user's local directory. The most important folders in this repository are: https://github.com/matrices Contains the operators expressed in the fine-structure basis, truncated at orbital angular momentum l={S,P,…,H}. This includes, for example: The total Hamiltonian The dipole operator These matrices are used throughout the simulations. @figures Contains the post-processed figures in .pdf and .svg format used in the manuscript. The simulation scripts save pre-processed figures in this folder, which are subsequently edited for final presentation.
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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