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Quantum Machine Learning
On the challenges of using D-Wave computers to sample Boltzmann Random Variables
arXiv
Authors: Thomas Pochart, Paulin Jacquot, Joseph Mikael
Year
2021
Paper ID
41142
Status
Preprint
Abstract Read
~2 min
Abstract Words
84
Citations
N/A
Abstract
Sampling random variables following a Boltzmann distribution is an NP-hard problem involved in various applications such as training of Boltzmann machines, a specific kind of neural network. Several attempts have been made to use a D-Wave quantum computer to sample such a distribution, as this could lead to significant speedup in these applications. Yet, at present, several challenges remain to efficiently perform such sampling. We detail the various obstacles and explain the remaining difficulties in solving the sampling problem on a D-wave machine.
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- Sampling random variables following a Boltzmann distribution is an NP-hard problem involved in various applications such as training of Boltzmann machines, a specific kind of...
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