Asked on May 16, 2016 in Machine Learning.
One of the major advantages to considering quantum computing – specifically quantum annealing for machine learning is that the solutions can be viewed as samples from a Boltzmann distribution. This is a very difficult computational process, that is typically approximated by a Markov Chain Monte Carlo method. If one can effectively sample from a Boltzmann distribution, then building probabilistic models based on the joint-probability distribution is in reach. This opens the doors to generative learning algorithms that can provide a much richer statistical framework for guiding experiments, performing model selection, model averaging etc. In short, with a quantum computer, one can integrate principles of probabilistic uncertainty into machine learning algorithms, as opposed to purely deterministic models.
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