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International Conference on Computational Intelligence and Network Systems (CINS)

Quantum Computing for Carbon Capture: A Qiskit-Based Framework and Perspective

K. Tarakeshwar, Syed Sufiyan, Param Pathak, Shalini Devendrababu, Adarsh Ganesan

Published Nov 25, 2025• 0 citations
Publisher

Abstract

Global climate policies demand more efficient transformations to advance carbon capture technologies. However, traditional classical computing techniques often face barriers in modeling complex CO2capture materials. Hence, quantum approaches have become popular in this direction. This paper reviews studies that utilize IBM’s Qiskit for addressing carbon capture technologies. We also showcase a Qiskit-based workflow using an illustrative example of a metal organic framework-based fragment (Mg − C − O). We present the code for defining the Mg − C − O geometry, building unitary coupled cluster singles and doubles ansatz, and computing the binding energy. We also showcase the associated quantum circuit for simulating the Mg − CO3molecule.

DOI
10.1109/cins67018.2025.11412198

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Global climate policies demand more efficient transformations to advance carbon capture technologies. However, traditional classical computing techniques often face barriers in modeling complex CO2capture materials. Hence, quantum approaches have become popular in this direction. This paper reviews studies that utilize IBM’s Qiskit for addressing carbon capture technologies. We also showcase a Qiskit-based workflow using an illustrative example of a metal organic framework-based fragment (Mg − C − O). We present the code for defining the Mg − C − O geometry, building unitary coupled cluster singles and doubles ansatz, and computing the binding energy. We also showcase the associated quantum circuit for simulating the Mg − CO3molecule.
Despite the recent progress in quantum computational algorithms for chemistry, there is a dearth of quantum computational simulations focused on material science applications, especially for the energy sector, where next generation sorbing materials are urgently needed to battle climate change. To drive their development, quantum computing is applied to the problem of CO2_22​ adsorption in Al-fumarate Metal-Organic Frameworks. Fragmentation strategies based on Density Matrix Embedding Theory are applied, using a variational quantum algorithm as a fragment solver, along with active space selection to minimise qubit number. By investigating different fragmentation strategies and solvers, we propose a methodology to apply quantum computing to Al-fumarate interacting with a CO2_22​ molecule, demonstrating the feasibility of treating a complex porous system as a concrete application of quantum computing. Our work paves the way for the use of quantum computing techniques in the quest of sorbents optimisation for more efficient carbon capture and conversion applications.
Abstract Despite the recent progress in quantum computational algorithms for chemistry, there is a dearth of quantum computational simulations focused on material science applications, especially for the energy sector, where next generation sorbing materials are urgently needed to battle climate change. To drive their development, quantum computing is applied to the problem of CO2 adsorption in Al-fumarate Metal-Organic Frameworks. Fragmentation strategies based on Density Matrix Embedding Theory are applied, using a variational quantum algorithm as a fragment solver, along with active space selection to minimise qubit number. By investigating different fragmentation strategies and solvers, we propose a methodology to apply quantum computing to Al-fumarate interacting with a CO2 molecule, demonstrating the feasibility of treating a complex porous system as a concrete application of quantum computing. We also present emulated hardware calculations and report the impact of device noise on calculations of chemical dissociation, and how the choice of error mitigation scheme can impact this type of calculation in different ways. Our work paves the way for the use of quantum computing techniques in the quest of sorbents optimisation for more efficient carbon capture and conversion applications.
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Direct air capture (DAC) of carbon dioxide is a promising method for mitigating climate change. Solid sorbents, such as metal–organic frameworks, are currently being tested for DAC application. However, their potential for deployment at scale has not been fully realized. The computational discovery of solid sorbents is challenging, given the vast chemical search space and the DAC requirements for molecular selectivity. Quantum computing can potentially accelerate the discovery of solid sorbents for DAC by predicting molecular binding energies. In this work, we explore algorithms for predicting gas adsorption in metal–organic frameworks using a quantum computer. In particular, we simulate the potential energy surfaces of CO2, N2, and H2O molecules at the Mg+2 metal center that represents the binding sites of typical metal–organic frameworks. We apply the qubit-ADAPT-VQE technique to run simulations on both classical and quantum computing hardware and achieve reasonable accuracy while maintaining hardware efficiency.
The need for creative ideas to address the growing challenges posed by climate change has become critical. This paper explores the various uses of quantum computing for mitigating climate change, with an emphasis on energy optimization, improved climate modeling, and weather forecasting. Environmental modeling, chemistry, material science, carbon capture techniques, and other significant applications have also been discussed. Finding important approaches, results, and associated technological constraints is the current state of study. Examining relevant case studies, the review suggests strategic directions for future research on climate change enabled by quantum computing. It serves as an essential tool for scientists, policymakers, and researchers, enabling a deeper comprehension of the potential of quantum computing in environmental science and encouraging multidisciplinary cooperation for the long-term mitigation of climate change.