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PC2, NHR@GWDG, and NHR@ZIB organize a Quantum Optimization tutorial at ISC25.
Please register at the ISC25 (https://isc-hpc.com/attendance/) to participate in the tutorial.
Details: https://isc.app.swapcard.com/event/isc-high-performance-2025/planning/UGxhbm5pbmdfMjU4MTgxMQ==
Abstract:
Quantum computing is a rapidly evolving technology with significant potential, particularly for solving optimization problems. Hard-to-solve classical challenges, such as graph-based problems, hold high practical relevance. A notable example is task mapping, where quantum approaches could provide groundbreaking solutions. Currently, hybrid methods such as the Quantum Approximate Optimization Algorithm (QAOA) are especially promising as they circumvent some of the disadvantages of noisy intermediate-scale quantum computers. This tutorial has two main parts. The first part is an introduction to quantum computing, which familiarizes beginners with the concepts and programming of gate-based quantum computers. This section provides intuitive explanations and beginner-friendly tutorials, ensuring no prior knowledge of quantum mechanics is required. The Qiskit programming framework will be used in the hands- on tutorials to deepen the understanding of quantum computing. Quantum simulators will be used to simulate, analyze, and understand the action of quantum gates and how typical quantum algorithms are designed. The second part provides an introduction to solving optimization problems with the QAOA approach, a quantum algorithm designed for solving combinatorial optimization problems by integrating gate-based quantum circuits and classical optimization. It will show how the QAOA approach is rooted in the variational quantum eigensolver framework and how it can leverage parameterized quantum circuits to tackle NP-hard problems with potential quantum speedups. The interplay between cost and mixer Hamiltonians and the optimization of circuit parameters will be discussed. As a practical example, we will focus on a problem well known to many attendees from the field of HPC, namely mapping tasks to machines. This problem can be generalized to a graph optimization problem, which has high practical, theoretical and educational relevance and is familiar to most IT scientists, broadening the appeal of the tutorial. As in our previous successful ISC and SC tutorials on quantum machine learning we will provide a mixture of presentations and practical exercises to increase participant engagement. By the end of this tutorial, participants will have a hands-on understanding of quantum computing, its challenges, and its potential to revolutionize optimization problems like those encountered in HPC. In addition, they will gain the ability to identify whether the optimization problems they encounter might be more efficiently solvable using QAOA.
Format: On Site
Targeted Audience: This tutorial targets a broad audience, including HPC users, IT scientists, and early-career researchers. It introduces quantum computing concepts for beginners, requiring only basic Python and linear algebra knowledge. The second part covers advanced quantum optimization with QAOA, allowing participants to build on the first part, or join directly.
Beginner Level: 50%
Intermediate Level: 50%