pmirallesr t1_irnmvpg wrote
You're thinking about implementing classic ml (backprop) in qc. Proponents of quantum ML look for alternative ways of "machine learning", either not calculating gradients, or trying to exploit the properties of quantum mechanics to "learn better". If it all sounds very fuzzy it's because it is
avialex OP t1_irnnm6a wrote
They certainly are looking, but at the same time gradient calculation is fundamental to how quantum neural networks are implemented right now, and QNN's are a relatively active area of study. I don't think we can dismiss the work in the field as it stands, because it's all built on the foundation of gradient descent. Afaik no one has yet found a better way to train a QNN, even on quantum data. I could be wrong.
pmirallesr t1_irnqpq4 wrote
That's fair. I liked Maria Schuld's research on QML.
gosh-darnit- t1_irnpn8b wrote
This is my understanding as well. Quantum computing offers another computing paradigm, which opens up new possibilities. There's little point of thinking of algorithms that work well using the current computer paradigm.
Unfortunately I know too little of QML to give specific examples of what new opportunities it might provide.
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