Training Computer Scientists for the Challenges of Hybrid Quantum-Classical Computing

TL;DR

A new lecture and exercise series on Hybrid Quantum-Classical Systems, where students learn how to decompose applications and implement computational tasks on a hybrid quantum-classical computational continuum.

Abstract

As we enter the post-Moore era, we experience the rise of various non-von-Neumann-architectures to address the increasing computational demand for modern applications, with quantum computing being among the most prominent and promising technologies. However, this development creates a gap in current computer science curricula since most quantum computing lectures are strongly physics-oriented and have little intersection with the remaining curriculum of computer science. This fact makes designing an appealing course very difficult, in particular for non-physicists. Furthermore, in the academic community, there is consensus that quantum computers are going to be used only for specific computational tasks (e.g., in computational science), where hybrid systems - combined classical and quantum computers - facilitate the execution of an application on both quantum and classical computing resources. A hybrid system thus executes only certain suitable parts of an application on the quantum machine, while other parts are executed on the classical components of the system. To fully exploit the capabilities of hybrid systems and to meet future requirements in this emerging field, we need to prepare a new generation of computer scientists with skills in both distributed computing and quantum computing. To bridge this existing gap in standard computer science curricula, we designed a new lecture and exercise series on Hybrid Quantum-Classical Systems, where students learn how to decompose applications and implement computational tasks on a hybrid quantum-classical computational continuum. While learning the inherent concepts underlying quantum systems, students are obligated to apply techniques and methods they are already familiar with, making the entrance to the field of quantum computing comprehensive yet appealing and accessible to students of computer science.

Figures (4)

• Figure 1: Example of a quantum circuit. Each horizontal line represents a qubit, with the (temporal) order of operations progressing from left to right. Here, all qubits are initialized in the state "$\left|0\right\rangle$", before a sequence of single-qubit Hadamard gates (white boxes, "H"), and two-qubit controlled-$Z$ gates (vertical lines with black circles indicating the respective qubit pairs) is applied. The resulting three-qubit state is the (genuinely multipartite entangled) Greenberger-Horne-Zeilinger (GHZ) state $\bigl(\left|000\right\rangle+\left|111\right\rangle\bigr)/\sqrt{2}$.
• Figure 2: After completing the course, students should be able to describe and implement the depicted execution models for quantum computation. The (a) Single Execution Model performs one single run of a quantum circuit, whereas the (b) Job Execution Model carries out the same circuit repeatedly to obtain statistics of different outcomes from which a solution is interpreted. In both models, the classical computers involved are used to interface with and control the quantum system. The (c) Hybrid Execution Model further relies on classical computers to execute computational parts of an algorithm, such as optimization subroutines in VQAs.
• Figure 3: Variational quantum algorithms.
• Figure 4: Scientific workflow decomposition into a hybrid quantum-classical workflow. Here, for example, the computation of interatomic distances and largest eigenvalues in the (a) initial workflow are identified as suitable candidates for implementation on a quantum computer in a (b) hybrid workflow.