Relatore: Giuliana Siddi Moreau, CRS4
JUNIQ: HPC-QCS infrastructure for practical quantum computing Kristel Michielsen Institute for Advanced Simulation Jülich Supercomputing Centre Forschungszentrum Jülich D-52425 Jülich Germany RWTH Aachen University 52056 Aachen Germany E-mail: firstname.lastname@example.org Quantum computing promises unprecedented possibilities for important computing tasks such as quantum simulations in chemistry and materials science or as optimization and machine learning, which can considerably change science, industry, economy and our everyday life. With this potential, quantum computing is increasingly attracting interest from industry and scientific groups 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. The systems may be used as stand-alone computing devices for experimental and development purposes or deeply integrated as accelerators, i.e., as quantum processing units, in existing HPC infrastructures to carry out first practical computations.
The Jülich UNified Infrastructure for Quantum computing (JUNIQ), a quantum computer user facility which is set up at the Jülich Supercomputing Centre (JSC), integrates quantum computing devices with various quantum technology readiness levels into the modular supercomputing architecture of JSC. Within JUNIQ, user support and training in HPC and quantum computer usage is provided, software tools, modelling concepts and algorithms are developed, and it plays an important role in the development of prototype applications. 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 for aircraft industry. In addition, we discuss some results for the companion planting optimization problem and some preliminary results for prototype Earth-Observation applications, which are based on quantumclassical classification and regression algorithms for remote sensing data