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One of the main obstacles to advancing green technologies, such as bio-catalysis for efficient ammonia production, artificial photosynthesis, or loss-less energy transmission via superconductors, is the complexity of their underlying quantum mechanical mechanisms. These processes, enabled by small quantum systems with strong electronic correlations, are not yet understood and pose significant challenges to experimental and theoretical investigations. Hence, there is a pressing need for sophisticated computational methods to elucidate these phenomena and support the development of green technologies. For example, artificial nitrogen fixation — converting molecular nitrogen into ammonia — at lower temperatures and pressures would offer a more sustainable alternative to the energy-intensive Haber-Bosch process, drastically reducing greenhouse gas emissions.
Fortunately, in quantum chemistry and solid-state physics, the fundamental equation that describes these processes — the Schrödinger equation — is at hand. A solution of the correlated motion of electrons — given by its wavefunction Ψ(x) — would allow a description of groundbreaking physical and chemical phenomena, including nitrogen fixation, photosynthesis and superconductivity. An accurate fundamental understanding of these processes at the quantum scale would enable a bottom-up materials design approach to mimic them artificially. However, due to the correlation between electrons, the Schrödinger equation is not analytically solvable, as the size of the exact wavefunction Ψ(x) grows exponentially with the problem size.
In our group, we develop a synergistic high-performance computing and quantum computing approach aided by novel artificial intelligence/deep machine learning methods to enable the computational study of complex quantum systems relevant to the green energy transition. Additionally, our current research focuses on developing innovative quantum Monte Carlo methods and novel quantum computing algorithms to enable realistic electronic structure calculations for strongly correlated electron problems on high-performance computing hardware and near-term quantum computing devices.
Dr. Werner Dobrautz
CASUS Research Team Leader
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+49 3581 37523 59
Center for Advanced Systems Understanding
Conrad-Schiedt-Straße 20
D-02826 Görlitz
Within the AI 4 Quantum project, we develop and apply novel machine learning (ML) approaches to enable the computational study of complex quantum systems relevant to the green energy transition. The groups’ research foci are:
In the research project qHPC-GREEN, we aim to address the challenges posed by climate change and the imperative for a green energy transition by leveraging the synergistic potential of high-performance computing (HPC) and quantum computing (QC) to model quantum mechanical systems pivotal to biochemical and physical phenomena relevant to environmental and energy challenges.
One of the main obstacles to advancing green technologies, such as bio-catalysis for efficient ammonia production, is the complexity of their underlying quantum mechanical mechanisms. These processes, catalyzed by small quantum systems with strong electronic correlations, are not yet fully understood and pose significant challenges to experimental and theoretical investigation. Hence, there is a pressing need for sophisticated computational models to elucidate these phenomena and support the development of effective catalysts and other green technologies. Especially artificial enzymatic nitrogen fixation at lower temperatures and pressures would offer a more sustainable alternative to the energy-intensive Haber-Bosch process, thereby drastically reducing greenhouse gas emissions.
In this project, we aim to bridge this gap through an innovative approach that combines the strengths of both classical and novel quantum computing hardware. The project aims to develop a seamless hybrid HPC+QC methodology tailored to accurately describe quantum materials and bio-inspired catalysts. This approach includes a correlated method to minimize quantum resources and a divide-and-conquer strategy utilizing HPC for weakly-correlated and QC for strongly-correlated regions. The seamless HPC+QC computational toolset will aid in understanding biological nitrogen fixation performed by the nitrogenase enzyme and its iron-molybdenum cofactor.
This project aims to create algorithms that exploit the unique advantages of QC, particularly in treating small, strongly correlated regions that are challenging for classical computations. Integrating QC solutions with conventional HPC approaches will create a powerful computational toolset expected to address complex environmental science research problems beyond the capabilities of existing computational methods.
„Sponsored by the Federal Ministry of Education and Research“
„Research Program Quantum Systems“
Subproject qHPC-GREEN
License plate number 13N17229
Anthony Gandon, Alberto Baiardi, Max Rossmannek, Werner Dobrautz, Ivano Tavernelli - arXiv 19 Dec 2024
Exploiting inherent symmetries is a common and effective approach to speed up the simulation of quantum systems. However, efficiently accounting for non-Abelian symmetries, such as the SU(2) total-spin symmetry, remains a major challenge. In fact, expressing total-spin eigenstates in terms of the computational basis can require an exponentially large number of coefficients. In this work, we introduce a novel formalism for designing quantum algorithms directly in an eigenbasis of the total-spin operator…
Center for Advanced Systems Understanding
Untermarkt 20, D-02826 Görlitz
Conrad-Schiedt-Str. 20, D-02826 Görlitz
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