The use of quantum computers in industry is still in its infancy.However, the successful collaboration with Covestro AG shows that companies can already benefit from quantum mechanics and quantum-inspired methods.

HQS Quantum Simulations follows a structured approach in its projects to create both short-term and long-term added value. Using this aaproach, we have been working intensively with Covestro AG for many years . The central problem of the project is the simulation of molecules.

Quantum computers are seen as the key technology of the future, but their concrete industrial applications have not yet been fully explored. Our goal at HQS Quantum Simulations is to develop realistic and practical quantum applications that offer real added value for companies. To pursue this, HQS Quantum Simulations has been working with the science and technology company Merck KGaA, Darmstadt Germany, since 2019.

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).

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.

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.