Quantum simulation of CO$_2$ chemisorption in an amine-functionalized metal-organic framework
- URL: http://arxiv.org/abs/2504.17453v1
- Date: Thu, 24 Apr 2025 11:35:21 GMT
- Title: Quantum simulation of CO$_2$ chemisorption in an amine-functionalized metal-organic framework
- Authors: Jonathan R. Owens, Marwa H. Farag, Pooja Rao, Annarita Giani,
- Abstract summary: We perform a series of calculations using simulated QPUs, accelerated by NVIDIA-Q platform.<n>We focus on a molecular analog of an amine-functionalized metal-organic framework (MOF) -- a promising class of materials for CO2 capture.<n>We explore active spaces of (6e,6o), (10e,10o), and (12e12o), corresponding to 12, 20, and 24 qubits, respectively.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We perform a series of calculations using simulated QPUs, accelerated by NVIDIA CUDA-Q platform, focusing on a molecular analog of an amine-functionalized metal-organic framework (MOF) -- a promising class of materials for CO2 capture. The variational quantum eigensolver (VQE) technique is employed, utilizing the unitary coupled-cluster method with singles and doubles (UCCSD) within active spaces extracted from the larger material system. We explore active spaces of (6e,6o), (10e,10o), and (12e,12o), corresponding to 12, 20, and 24 qubits, respectively, and simulate them using CUDA-Q's GPU-accelerated state-vector simulator. Notably, the 24-qubit simulations -- among the largest of their kind to date -- are enabled by gate fusion optimizations available in CUDA-Q. While these active space sizes are among the largest reported in the context of CO2 chemisorption, they remain insufficient for a fully accurate study of the system. This limitation arises from necessary simplifications and scalability challenges of VQE, particularly the barren plateau problem. Nonetheless, this work demonstrates the application of VQE to a novel material system using large-scale simulated QPUs and offers a blueprint for future quantum chemistry calculations.
Related papers
- Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML)
At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
arXiv Detail & Related papers (2024-11-13T23:56:20Z) - Efficient charge-preserving excited state preparation with variational quantum algorithms [33.03471460050495]
We introduce a charge-preserving VQD (CPVQD) algorithm, designed to incorporate symmetry and the corresponding conserved charge into the VQD framework.
Results show applications in high-energy physics, nuclear physics, and quantum chemistry.
arXiv Detail & Related papers (2024-10-18T10:30:14Z) - Validating Large-Scale Quantum Machine Learning: Efficient Simulation of Quantum Support Vector Machines Using Tensor Networks [17.80970950814512]
We present an efficient tensor-network-based approach for simulating large-scale quantum circuits.<n>Our simulator successfully handles QSVMs with up to 784 qubits, completing simulations within seconds on a single high-performance GPU.
arXiv Detail & Related papers (2024-05-04T10:37:01Z) - Size-consistency and orbital-invariance issues revealed by VQE-UCCSD calculations with the FMO scheme [0.0]
fragment molecular orbital (FMO) scheme is one of the popular fragmentation-based methods.
We used a GPU-accelerated quantum simulator (cuQuantum) to perform the electron correlation part of the FMO calculation.
arXiv Detail & Related papers (2024-02-28T02:16:14Z) - Quantum Simulations for Carbon Capture on Metal-Organic Frameworks [0.0]
DAC is a technical solution that does not rely on natural processes to capture CO2 from the atmosphere.
We aim to leverage the potential of quantum computing to improve the filters used in DAC.
arXiv Detail & Related papers (2023-11-21T07:58:02Z) - A self-consistent field approach for the variational quantum
eigensolver: orbital optimization goes adaptive [52.77024349608834]
We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE)
This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
arXiv Detail & Related papers (2022-12-21T23:15:17Z) - Towards Neural Variational Monte Carlo That Scales Linearly with System
Size [67.09349921751341]
Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors.
The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems.
We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
arXiv Detail & Related papers (2022-12-21T19:00:04Z) - Photonic Quantum Computing For Polymer Classification [62.997667081978825]
Two polymer classes visual (VIS) and near-infrared (NIR) are defined based on the size of the polymer gaps.
We present a hybrid classical-quantum approach to the binary classification of polymer structures.
arXiv Detail & Related papers (2022-11-22T11:59:52Z) - Quantum Davidson Algorithm for Excited States [42.666709382892265]
We introduce the quantum Krylov subspace (QKS) method to address both ground and excited states.
By using the residues of eigenstates to expand the Krylov subspace, we formulate a compact subspace that aligns closely with the exact solutions.
Using quantum simulators, we employ the novel QDavidson algorithm to delve into the excited state properties of various systems.
arXiv Detail & Related papers (2022-04-22T15:03:03Z) - Localized Quantum Chemistry on Quantum Computers [0.6649973446180738]
Quantum chemistry calculations are typically limited by the computation cost that scales exponentially with the size of the system.
We present a quantum algorithm that combines a localization of multireference wave functions of chemical systems with quantum phase estimation.
arXiv Detail & Related papers (2022-03-03T20:52:22Z) - Toward Practical Quantum Embedding Simulation of Realistic Chemical
Systems on Near-term Quantum Computers [10.26362298019201]
We numerically test the method for the hydrogenation reaction of C6H8 and the equilibrium geometry of the C18 molecule, with basis sets up to cc-pVDZ.
Our work implies the possibility of solving industrial chemical problems on near-term quantum devices.
arXiv Detail & Related papers (2021-09-16T15:44:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.