Quantum computing has become one of the most promising technologies to solve problems in quantum simulations for materials and chemicals, optimization and machine learning. Those solutions will contribute to a sustainably growing society. Applications such as vehicle routing, power trading, supply chain network optimization, cancer radiation treatment scheduling, and target interactions for drug design constitute current optimization challenges. Computing methods like machine learning and data mining, as well as creating artificial intelligence and computer vision are also rooted in the field of optimization. The research group Quantum Information Processing (QIP) works on benchmarking state-of-the-art quantum computers, simulating quantum systems and developing prototype applications and use cases for quantum computing.

QIP performs benchmarking by implementing optimization algorithms such as the Quantum Approximation Optimization Algorithm (QAQA) and the Variational Quantum Eigensolver (VQE) on various gate-based noisy-intermediate-state-quantum (NISQ) devices. Their performances are cross-platform validated and compared to results generated by emulators of ideal quantum computers such as the Jülich Universal Quantum Computer Simulator (JUQCS). Additionally, analog quantum computers, such as quantum annealers and quantum simulators, are included in the cross-platform benchmarking process. QIP works on integrating various quantum devices into the Modular Supercomputer Architecture of the Jülich Supercomputing Centre to enable hybrid classical-quantum calculations, which are essential for practical quantum computing. Based on these benchmarking and integration efforts, the group seeks to identify and develop use cases and prototype applications that will help to solve large-scale problems in the near future. Besides algorithmic benchmarking, QIP simulates quantum systems, such as multi-qubit chips, to understand error sources and their affection on algorithm execution.