The Q-Solid grant is pioneering a quantum computing strategy in Germany's innovation hub. With 25 collaborators, it aims to build a robust solid-state quantum computer by mid-2024. HQS is responsible for WP9, which focuses on benchmarking and optimising algorithms that are crucial for applications such as nuclear magnetic resonance (NMR). HQS Quantum Simulations is helping to address NMR challenges by using quantum computing to simulate molecular NMR spectra. The collaboration addresses the effects of noise and improves the reliability of spectra. This synergistic effort is producing cutting-edge software that leverages quantum computing for innovative market applications.

In the quest for quantum computing applications in Germany's NISQ era, Q-Exa tackles the formidable hurdle of noise and errors. Focused on integrating quantum computing (QC) into high-performance computing (HPC), the project, led by a consortium featuring IQM, LRZ, HQS, and Atos, seeks to deliver a cutting-edge quantum demonstrator based on superconducting circuits. HQS, as a key player, addresses this challenge by exploring scientific questions and developing software tools. Leveraging the acquired HQS software framework, the team pioneers a technology allowing error-inclusive quantum hardware computation.

In collaboration with Bosch, BASF SE, Friedrich-Alexander-Universität Erlangen, and Heinrich-Heine-Universität Düsseldorf, HQS leads the MANIQU project. Focused on quantum material simulations, HQS introduces the HQS Spin Mapper for transition metal-containing materials. Addressing challenges in simulating strongly correlated electron systems, the project explores NISQ devices' accuracy for material properties.

The AutoQML project aims to develop a Python-based software package for automated quantum machine learning. HQS addresses challenges by creating a versatile library, AutoSelectionBackend, optimizing quantum backend selection based on machine quality or execution speed. The solution integrates into AutoQML's sQUlearn framework, enhancing accessibility and efficiency in quantum computing. This collaborative effort not only advances quantum machine learning but also fosters a supportive community, bridging the gap for both novices and experts in the field.