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Training a quantum annealing based restricted Boltzmann machine on cybersecurity data
In the second scheme, a RBM is used to generate synthetic data to balance the training dataset.
Comparison of D-Wave Quantum Annealing and Classical Simulated Annealing for Local Minima Determination
Apparently, the size of the BoA is not or at least is less important for QA search compared to the classical search, allowing QA to easily find many potentially important (e. g., wide and deep) LVs missed by even prohibitively lengthy classical searches.
Towards Sampling from Nondirected Probabilistic Graphical models using a D-Wave Quantum Annealer
For the OptDigits dataset, the states in the D-Wave sample belonged to about two times more local valleys compared to the MCMC sample.
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