Quantum annealing task mapping for heterogeneous computing systems

Kenzie Ellenberger; Dylan Couch; Jeffrey Greer; Noah Gregory; Luis Sanchez; Kaleb Love; Yaroslav Koshka; Samee U. Khan

doi:10.1117/12.3029949

2 August 2024 Quantum annealing task mapping for heterogeneous computing systems

Kenzie Ellenberger, Dylan Couch, Jeffrey Greer, Noah Gregory, Luis Sanchez, Kaleb Love, Yaroslav Koshka, Samee U. Khan

Proceedings Volume 13106, Photonics for Quantum 2024; 1310609 (2024) https://doi.org/10.1117/12.3029949
Event: Photonics for Quantum, 2024, Waterloo, Ontario, Canada

Conference Poster

Abstract

Heterogeneous computing (HC) systems are essential parts of modern-day computing architectures such as cloud, cluster, grid, and edge computing. Many algorithms exist within the classical environment for mapping computational tasks to the HC system’s nodes, but this problem is not well explored in the quantum area. In this work, the practicality, accuracy, and computation time of quantum mapping algorithms are compared against eleven classical mapping algorithms. The classical algorithms used for comparison include A-star (A*), Genetic Algorithm (GA), Simulated Annealing (SA), Genetic Simulated Annealing (GSA), Opportunistic Load Balancing (OLB), Minimum Completion Time (MCT), Minimum Execution Time (MET), Tabu, Min-min, Maxmin, and Duplex. These algorithms are benchmarked using several different test cases to account for varying system parameters and task characteristics. This study reveals that a quantum mapping algorithm is feasible and can produce results similar to classical algorithms.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kenzie Ellenberger, Dylan Couch, Jeffrey Greer, Noah Gregory, Luis Sanchez, Kaleb Love, Yaroslav Koshka, and Samee U. Khan "Quantum annealing task mapping for heterogeneous computing systems", Proc. SPIE 13106, Photonics for Quantum 2024, 1310609 (2 August 2024); https://doi.org/10.1117/12.3029949

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