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Quantum Computing Based Design of Multivariate Porous Materials.

Shinyoung Kang1, Younghun Kim1, Jihan Kim1

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This summary is machine-generated.

We developed a quantum computing model to predict the ground-state configurations of multivariate porous materials. This approach efficiently searches vast design spaces, enabling rational material design.

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Area of Science:

  • Materials Science
  • Quantum Computing
  • Computational Chemistry

Background:

  • Multivariate (MTV) porous materials offer synergistic properties due to complex structures.
  • Predicting MTV ground-state configurations is challenging due to design complexity.

Purpose of the Study:

  • To develop a quantum computing Hamiltonian model for efficient MTV configuration prediction.
  • To enable rational design of MTV porous materials by optimizing configurations.

Main Methods:

  • A graph-based Hamiltonian model encoding linker types as qubits was proposed.
  • A variational quantum circuit using the Sampling Variational Quantum Eigensolver (VQE) was implemented.
  • The model was validated using simulations and executed on IBM quantum hardware.

Main Results:

  • The quantum model successfully reproduced ground-state configurations of known MTV materials.
  • The framework efficiently represents exponentially large design spaces with linear qubit resources.
  • Quantum calculations were validated on a 127-qubit IBM quantum processor.

Conclusions:

  • The proposed quantum computing approach is a valid first step toward practical algorithms for rational porous material design.
  • This method offers efficient exploration of complex material design spaces.