Fraunhofer Institute for Mechanics of Materials IWMThe Baden-Württemberg Quantum Computing Competence Center (KQCBW) aims to further develop quantum computing - a key technology - in Germany and to implement practical applications across various industries.
Baden-Württemberg Quantum Computing Competence Center
Completed research project
Project description
The Baden-Württemberg Quantum Computing Competence Center (KQCBW) was founded in 2020 to promote the development and application of quantum computers in key economic sectors such as information technology, medical engineering, chemistry, mechanical engineering, manufacturing, the automotive industry and logistics. As part of the QuantumBW state initiative’s executive office, the KQCBW plays an active role in expanding Baden-Württemberg as a hub for quantum technology.
The goal of the ten-month KQCBW24 transfer project was to ensure access to state-of-the-art quantum computers, such as IBM systems, and to develop innovative tools and algorithms for utilizing quantum computing technology.
In addition to the IBM systems, the project granted all KQCBW partners access to an NV-based quantum computer as well as the high-performance computing (HPC) simulation infrastructure, plus the virtual demonstrator platform for simulating quantum algorithms. This comprehensive quantum computing infrastructure provides KQCBW partners with ideal conditions for their research work.
Fraunhofer IWM subproject:
Fraunhofer IWM covered the entire spectrum from hardware modeling to application formulation and algorithm development. The institute modeled the dissipative influence of crystal structure defects surrounding NV color centers in diamond, thereby parameterizing the theoretical model in a material-specific and application-oriented manner - an indispensable foundation for the project partners’ experimental pulse optimization. To this end, Fraunhofer IWM formulated two materials science use cases: first, the solution of the correlated auxiliary model within the framework of Dynamical Mean-Field Theory (DMFT) to calculate the electronic properties of transition metal oxides (relevant for fuel cell electrodes and high-temperature superconductors), and second, the Hamiltonian simulation of the Heisenberg spin model to describe magnetic properties (relevant for new memory architectures such as racetrack memory).
For these use cases, Fraunhofer IWM has developed specific quantum algorithms: time-evolution algorithms with shortened circuits for DMFT as well as Variational Quantum Imaginary Time Evolution (VarQITE) as a scalable, NISQ-compatible approach for calculating ground-state properties of strongly correlated systems. In addition, Fraunhofer IWM investigated advanced techniques such as block encoding and quantum signal processing.
Based on this project work, Fraunhofer IWM can offer industrial companies the following new R&D services:
- Quantum Computer-Aided Simulation of Strongly Correlated Functional Materials
Using a hybrid high-performance computing (HPC) and quantum computing (QC) simulation workflow (density functional theory (DFT) and dynamical mean-field theory (DMFT) with a quantum solver) enables more accurate predictions of electronic and magnetic materials properties than purely classical methods, thereby facilitating faster, more targeted materials development with reduced experimental costs. - Evaluation and feasibility studies on the quantum advantage in materials development
For companies seeking to evaluate whether quantum computing offers added value for their specific materials challenges, Fraunhofer IWM provides its benchmarking expertise: comparison of quantum algorithms (Variational Quantum Inverse Time Evolution (VarQITE), time evolution and Quantum Subspace Variational Time Evolution (QSVT)) with state-of-the-art classical methods for specific materials systems. - Materials-specific modeling of defects and dissipation in qubit materials
Atomistic simulations of crystal defects help understand and reduce the interaction between qubits and their materials-specific environment. Targeted optimization of qubit coherence and gate quality is achieved through materials-science-based parameter selection.
