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.
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.
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.
Direct air capture of Carbon Dioxide is a technical solution that does not rely on natural processes to capture CO2from the atmosphere. In DAC, the filter material is designed to specifically bind CO2molecules. Hence a high-capacity filter is sought. We aim to leverage the potential of quantum computing to improve the filters used in DAC. Metal-Organic Frameworks (MOFs) have high surface area and tunable pore sizes which makes them an attractive material for gas storage and separation. Using the variational quantum eigensolver (VQE) algorithm, we find the minimum of the potential energy surface (PES) by first considering only the active site of the MOF (the metal ion). For complex systems, we employ Density Matrix Embedding Theory and use VQE as a fragment solver at the binding site. Techniques like deparameterisation are used to minimise the count of trainable parameters. We present results of ideal and noisy simulations as well as from a real hardware device. Resources are estimated for MOFs unit cell. The findings from our study demonstrates the potential of quantum computing to effectively perform quantum simulations of strongly correlated fragments.
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. 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.
Carbon capture is vital for decarbonizing heavy industries such as steel and chemicals. Metal-organic frameworks (MOFs), with their high surface area and structural tunability, are promising materials for CO2 capture. This study focuses on Fe-MOF-74, a magnetic Mott insulator with exposed metal sites that enhance CO2 adsorption. Its strongly correlated electronic structure challenges standard DFT methods, which often yield inconsistent predictions. We initially benchmark adsorption energies using various DFT functionals, revealing substantial variability and underscoring the need for more accurate approaches, such as those provided by quantum computing. However, practical quantum algorithms are far less established for simulations of periodic materials, particularly when the plane-wave basis set, often comprising tens of thousands of basis vectors, is used. To address this, we employ an active space reduction strategy based on Wannier functions and natural orbital selection. Localized orbitals around the adsorption site are identified, and MP2 natural orbitals are used to improve convergence of correlation energies. Adsorption energies are then computed using a quantum number-preserving ansatz within the variational quantum eigensolver framework. In addition to classical simulations, we conduct quantum experiments using the sample-based quantum diagonalization method. Although current hardware limits the size of feasible simulations, our approach offers a more efficient and scalable path forward. These results advance the applicability of quantum algorithms to realistic models of carbon capture and periodic materials more broadly.
We perform a series of calculations using simulated quantum processing units (QPUs), accelerated by the NVIDIA CUDA-Q platform, focusing on a molecular analog of an amine-functionalized metal–organic framework, a promising class of materials for CO2 capture. The variational quantum eigensolver (VQE) technique is employed, utilizing both the unitary coupled-cluster method with singles and doubles and adaptive Ansätze within active spaces extracted from the larger material system. We explore active spaces of (6e, 6o), (8e, 8o), and (10e, 10o), corresponding to 12, 16, and 20 qubits, respectively, and simulate them using CUDA-Q’s graphics processing unit-accelerated state-vector simulator. Gate fusion is shown to decrease the circuit evaluation time by 2–3×, while parameter shift decreases the number of epochs required for variational convergence. The ADAPT-VQE method decreases both the number of epochs required for convergence and the number of circuit parameters across all active spaces, at the cost of an increased number of circuit evaluations. Combining ADAPT-VQE with the one- and two-electron integrals from a classical complete-active space self-consistent field calculation recovers more correlation energy, at the cost of increased computational time. The CO2 binding energy is computed, and we observe and discuss how just increasing the active space size can lead to uneven recovery of correlation energy, making the predicted binding energies variable, even positive (i.e., energetically unfavorable) in some instances. This can be partially remedied by using an alternative approach to computing the binding energy that more evenly spreads the active space. This work explores the application of VQE to a novel material system using large-scale simulated QPUs and provides some insight into various important considerations when performing these types of calculations, ultimately highlighting the challenges of studying chemisorption on near-term quantum machines, simulated or not.
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.