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Quantum Optimization
Balanced k-Means Clustering on an Adiabatic Quantum Computer
arXiv
Authors: Davis Arthur, Prasanna Date
Year
2020
Paper ID
21568
Status
Preprint
Abstract Read
~2 min
Abstract Words
93
Citations
N/A
Abstract
Adiabatic quantum computers are a promising platform for approximately solving challenging optimization problems. We present a quantum approach to solving the balanced k-means clustering training problem on the D-Wave 2000Q adiabatic quantum computer. Existing classical approaches scale poorly for large datasets and only guarantee a locally optimal solution. We show that our quantum approach better targets the global solution of the training problem, while achieving better theoretic scalability on large datasets. We test our quantum approach on a number of small problems, and observe clustering performance similar to the best classical algorithms.
Why This Paper Matters
- This paper contributes to the Quantum Optimization research area in the Quantum Articles archive.
- It adds a 2020 reference point for readers tracking recent quantum research.
- Adiabatic quantum computers are a promising platform for approximately solving challenging optimization problems.
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