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Algorithm engineering for a quantum annealing platform

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arxiv 1410.2628 v2 pith:Q6G63GDQ submitted 2014-10-09 cs.DS cs.ETquant-ph

Andrew D. King , Catherine C. McGeoch This is my paper · ORCID
classification cs.DScs.ETquant-ph
keywords quantumalgorithmannealingplatformadvancesapproachesbringcombinatorial
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Recent advances bring within reach the viability of solving combinatorial problems using a quantum annealing algorithm implemented on a purpose-built platform that exploits quantum properties. However, the question of how to tune the algorithm for most effective use in this framework is not well understood. In this paper we describe some operational parameters that drive performance, discuss approaches for mitigating sources of error, and present experimental results from a D-Wave Two quantum annealing processor.

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Cited by 1 Pith paper

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  1. Quantum Annealing Hyperparameter Analysis for Optimal Sensor Placement in Production Environments

    cs.ET 2025-07 conditional novelty 4.0 of 10

    A QUBO-based quantum annealing study shows that tuned hyperparameters and spectral decomposition improve sensor placement solutions on D-Wave, but classical solvers remain superior.

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