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Supporting documents: Capturing Protein Free Energy Landscape using Efficient Quantum Encoding

Ashwini Kannan, Jaya Vasavi Pamidimukkala, Avinash Dakshinamoorthy, Soham Bopardikar, Kalyan Dasgupta, Sanjib Senapati

Published Oct 16, 2026• 0 citations
Publisher

Abstract

This record contains the code and input files for our research presented in the paper "Capturing Protein Free Energy Landscape using Efficient Quantum Encoding". Contents: MD_input_files.zip contains the initial structure for each peptides considered, GROMACS topologies and input parameters required to run Molecular dynamics simulations. script.zip folder contains codes used to run VQE simulator and hardware runs script for post MD and quantum simulation free energy landscape analyses

DOI
10.5281/zenodo.23408671

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This record contains the code and input files for our research presented in the paper "Capturing Protein Free Energy Landscape using Efficient Quantum Encoding". Contents: MD_input_files.zip contains the initial structure for each peptides considered, GROMACS topologies and input parameters required to run Molecular dynamics simulations. script.zip folder contains codes used to run VQE simulator and hardware runs script for post MD and quantum simulation free energy landscape analyses
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 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.
Abstract Quantum computing in biology is one of the most rapidly evolving field of technology. Protein folding is one of the key challenges which requires accurate and efficient algorithms with a quick computational time. Structural conformations of proteins with disordered regions need colossal amount of computational resource to map its least energy conformation state. In this regard, quantum algorithms like Variational quantum eigensolver (VQE) are applied in the current research work to predict the lowest energy value of 50 peptides of 7 amino acids each. VQE is initially used to calculate the energy values over which Variational Quantum Optimization is applied via Conditional Value at Risk (CVaR) over 100 iterations of 500000 shots each to obtain least ground state energy value. This is compared to the molecular dynamics-based simulations of 50 nanoseconds each to calculate the energy values along with the folding pattern. The results suggest efficient folding outcomes from CvaR-VQE compared to MD based simulations. With the ever-expanding quantum hardware and improving algorithms the problem of protein folding can be resolved to obtain in depth insights on the biological process and drug design.
This record contains code, notebooks, data, and figure scripts associated with the project. The description below lists the contents of the upload and their roles. strongsym.zip: Zip archive used for the Zenodo upload. It contains the simul/, finsize_data/, and dependencies/ directories. simul/ : Simulation and analysis notebooks and scripts. MF_*.ipynb - Mean-field analysis notebooks. finite_size_spins.ipynb - Finite-size dynamics for 2 level system alt_finite_size_spins.ipynb - Alternative finite-size with a stress on Liouvullian spectrum analysis in the two level system. liouv_spec_spin1.ipynb -Finite-size spectrum analysis for three level model. find_symmetry.ipynb - Symbolic symmetry-finding notebook. finsize_data/ : Data files used to generate figures. fig_script.ipynb: Notebook to generate the figures overview_sketch.ipynb: Notebook to generate schmatics and diagrams (no raw data) dependencies/ : Environment specification files, for both python (.yml) and julia (.toml) Usage: Unzip strongsym.zip, set up environments using the files in dependencies/, and run the notebooks in simul/. Data required for figures is in finsize_data/; figure scripts are at the repository root. For a more detailed walkthrough and documentation, see the GitHub repository: https://github.com/MarcNairn/spin_strong_symmetries_phase