Quantum computing promises to speed up the computation in classical machine learning algorithms. Quantum computing also promises to enable better machine learning in the presence of noise which is beneficial since in many real world applications data is noisy. It is known that classical algorithms which learn with noise produce better trained models which are are better at generalization and which are more accurate since local optima are avoided. These benefits will bring significant practical applications that will affect businesses going forwards. We can imagine machine learning models which are self-learning in real time from vast amounts of noisy data.
In contrast, quantum neural networks have all their consituent elements (neurons and training algorithms) executed on a quantum computer. A great challenge in implementing quantum neural networks is that quantum mechanics is linear but classical neural networks require nonlinearities.
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It is now the consensus that general purpose quantum computation is within a 15 year time line