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Open Quantum Systems Decoherence
Quantum Machine Learning
Parameter Setting for Quantum Annealers
arXiv
Authors: Kristen L. Pudenz
Year
2016
Paper ID
42264
Status
Preprint
Abstract Read
~2 min
Abstract Words
84
Citations
N/A
Abstract
We develop and apply several strategies for setting physical parameters on quantum annealers for application problems that do not fit natively on the hardware graph. The strategies are tested with a culled random set of mixed satisfiability problems, yielding results that generalize to guidelines regarding which parameter setting strategies to use for different classes of problems, and how to choose other necessary hardware quantities as well. Alternate methods of changing the hardware implementation of an application problem are also considered and their utility discussed.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2016 reference point for readers tracking recent quantum research.
- We develop and apply several strategies for setting physical parameters on quantum annealers for application problems that do not fit natively on the hardware graph.
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