Many technical advances have been applied in the area High-Performance Computing for the first time, and this also seems to hold for Quantum Computing. Traditionally, however, experts in these fields come from a diverse educational background. Recently, Technische Hochschule Deggendorf (Deggendorf Institute of Technology, DIT) introduced a new master’s program „High-Performance Computing / Quantum Computing“ to offer students a direct way into the application specific aspects of these field of expertise. To our knowledge, this is the first course of studies of its kind. This work describes this new program of studies and tentatively identifies some of the first lessons learned so far.

Transport phenomena remains nowadays still the most challenging unsolved problem in computational physics, though the high-performance computing has been applied. Using classical simulations of accident scenarios in advanced reactors as example, the advantage and disadvantage of current multi-physics code systems coupling reactor kinetics and fluid dynamics are shortly presented. As the tomorrow’ technology, quantum computing opens however a grand new perspective for numerical simulations for transport phenomena. Taking fluid mechanics as a concrete application, the possible quantum algorithms are intensive reviewed. The opportunities and challenges of quantum computing for simulating fluid are discussed and foreseen.

Using the Intel Quantum SDK, the quantum algorithms have been preliminarily investigated for fluid dynamics i.e. to solve the lattice Boltzmann equation which is a mesoscopic approach. The Quantum Lattice Boltzmann equation has been derived, the implementations to solve this equation are in progress. The possible algorithms are discussed for instance HHL, VQE, Hamiltonian etc. Among them “Quantum Walks” are composition of the scattering operator and the translating operator which is similar to the Lattice Boltzmann equation collision and streaming operator. Building upon this similarity between the Quantum Walks and Quantum Lattice Boltzmann, the ongoing master thesis work is to solve a simple fluid dynamics problem on a Quantum Computer.

An introduction of Intel Quantum Software Development Kit (SDK). The Intel Quantum Software Development Kit (SDK) is a full-stack software development kit with an LLVM-based C++ compiler and system software workflow that enables the usage of a single self-contained source file to coordinate “classical” and “quantum” logic. Variational algorithms are some of the most promising workloads for quantum computing systems. They pair classical optimizers with quantum programs, forming a feedback loop to find convergence on a solution. The Intel Quantum SDK targets these “hybrid” algorithms by utilizing its Quantum Runtime to coordinate the flow of data between the classical and quantum logic as the executable runs on a simulated qubit backend. The Intel Quantum SDK will be the way to access and program real Intel qubit chips in the future.

Computational Fluid Dynamics (CFD) is a key scientific and engineering research area that is computationally demanding. High Performance Computing (HPC) systems have been used over the years to crunch data in CFD but there is still massive need for fast large-scale simulations. It has been shown that Quantum Computing alongside HPC holds the potential of achieving greater speed up in CFD calculations. In this presentation, we demonstrate how Navier-Stokes equations can be simulated on a Quantum simulator. In our approach we first discretize a steady-state one dimensional Navier-Stoke equation and apply the HHL algorithm to transform it into quantum states, and then measure the corresponding results and compare the result to that of its numerical equivalent.

HPC centers are facing a swelling request for bigger software flexibility to provision speedier and more miscellaneous modernization in computational technical work. Containers, which use Linux kernel structures to allow a client to alternate their own software stack for that installed on the host, is a progressively common technique to offer this flexibility. In our research, we will configure and test the performance and functions of a low overhead containerized cluster with HPC workloads. We will use Intel Quantum Simulator (Intel-QS) as the final workload. Intel-QS simulates quantum circuits and takes advantage of multi-core and multinode architectures. The Intel-QS uses MPI protocol to handle communication between nodes in the cluster. When it comes to container engine, we will start with Podman or Singularity. Podman tool is daemon-less, and the containers can even be run as a non-privileged user. It is easy to secure the host kernel from breakout attacks and it works well with the MPI because it uses the fork/exec model for containers instead of the client/server model. Singularity is specifically built for scientific and high-performance use cases. It has built-in support for MPI. We will use Kubernetes for container orchestration in the cluster.

In the quantum phase transitions, an accurate representation from single-shot experimental momentum-space density images of ultracold quantum gases can be one challenge task due to the noisy images which make data extraction difficult. Introducing some deep learning techniques to denoise the experimental image, mapping two-dimensional phase diagram of Haldane model has shown improvement in attaining accurate results that is however impossible to achieve with conventional methods. In this study two different machine learning models are implemented and compared to study their accuracies using theoretical results as a reference.