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Trapped Ion Quantum Computing
Quantum Machine Learning
A Quantum Extension of Variational Bayes Inference
arXiv
Authors: Hideyuki Miyahara, Yuki Sughiyama
Year
2017
Paper ID
24537
Status
Preprint
Abstract Read
~2 min
Abstract Words
92
Citations
N/A
Abstract
Variational Bayes (VB) inference is one of the most important algorithms in machine learning and widely used in engineering and industry. However, VB is known to suffer from the problem of local optima. In this Letter, we generalize VB by using quantum mechanics, and propose a new algorithm, which we call quantum annealing variational Bayes (QAVB) inference. We then show that QAVB drastically improve the performance of VB by applying them to a clustering problem described by a Gaussian mixture model. Finally, we discuss an intuitive understanding on how QAVB works well.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2017 reference point for readers tracking recent quantum research.
- Variational Bayes (VB) inference is one of the most important algorithms in machine learning and widely used in engineering and industry.
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