Venue: Hörsaal FIAS EG 0.100

The options of quantum computing and benchmarks of some of the most powerful supercomputers will be presented by Kristel Michielsen. She will give an example from aircraft industry.

Quantum computing promises unprecedented possibilities for important computing tasks such as quantum simulations in chemistry and materials science or optimization and machine learning. With this potential, quantum computing is increasingly attracting interest from industry and scientific communities that use high performance computing (HPC) for their applications. These pilot users are primarily interested in testing whether available quantum computers today or in the foreseeable future are suitable for simulating increasingly complex systems, analyzing large data sets using machine learning methods or performing the hardest optimization task.

Access to quantum computer emulators running on HPC systems and to quantum computing systems at the forefront of development is the prerequisite for testing, benchmarking, algorithm and use case design activities, and first serious applications in scientific and engineering challenges.

Benchmarking quantum computers and algorithms requires emulators that can efficiently utilize the architecture of present day supercomputers. As the simulation of universal quantum computers requires a large number of matrix-vector updates, most of which are 2-component and 4-component tensor operations, the task of simulating quantum computers is an ideal candidate to profit from recent developments in the GPU industry. We present benchmarks of some of the most powerful supercomputers using the Jülich universal quantum computer simulator (JUQCS).

Practical application requires the integration of quantum computers into existing HPC infrastructures in the form of quantum-classical hybrid computing models.

The Jülich UNified Infrastructure for Quantum computing (JUNIQ), a manufacturer-independent quantum computing user facility established at the Jülich Supercomputing Centre (JSC) aims to address all these needs.

As example, we present benchmarking results for the quantum approximate optimization algorithm (QAOA) emulated on a supercomputer and for the D-Wave quantum annealers for the tail assignment problem, a planning problem from aircraft industry.