Quantum Simulation of Carbon Capture in Periodic Metal-Organic Frameworks

This repository contains data and codes needed to reproduce the results in the paper by D. Rocca et al., arXiv:2510.02550 (2025)

For any questions, feel free to reach out to corresponding author: bkang@posco-inc.com

Repository structure

• Fe-MOF-74-vasp/vdWDF2, Fe-MOF-74-vasp/B3LYPD3, etc.
  VASP input files required to reproduce the DFT calculations using different exchange–correlation functionals (vdW-DF2, B3LYP-D3, etc.). Each subdirectory contains inputs for structural relaxations and/or single-point calculations as well as the corresponding output files (OUTCAR).

• Mg-MOF-74-vasp/vdWDF2, Mg-MOF-74-vasp/B3LYPD3, etc.
  Analogous files for Mg-MOF-74.

• active_space_generation_vasp
  Input files used to generate the active spaces. Each subdirectory is named according to the system (CO2, MOF, or MOFCO2), the kinetic energy cutoff (ENCUT), and the natural-orbital occupation threshold used to select the virtual orbitals. A README file with additional information is provided in this directory.

• qnp_vqe_qiskit_simulator
  Each subdirectory, corresponding to the different systems (CO2, MOF, and MOFCO2), contains Qiskit-based Jupyter notebooks used to implement the quantum-number-preserving (QNP) ansatz for VQE.
  The pre-optimized parameters are provided in the corresponding optimized_params.txt files.

• fullci_reference_pyscf
  Full configuration interaction reference results for different spin multiplicities, organized into three subdirectories: singlet, triplet, and quintet.
  The one- and two-electron integrals used in all VQE and SQD calculations are provided in this directory.

• sample-based_quantum_diagonalization/SQD_qiskit_simulator
  Jupyter notebooks used to simulate sample-based quantum diagonalization (SQD) using Qiskit.

• sample-based_quantum_diagonalization/SQD_IBM_hardware
  Jupyter notebooks used to perform SQD on IBM quantum hardware.
  The transpiled circuits (.qpy files) and the sampled bitstrings (hwbackend_counts_reducedgates.npy) are also provided.