Entangled Neural Networks & privacy preserving deep learning

There's a natural synergy between quantum computing and neural networks - a collection of entangled particles have correlated state - so instead of moving through the very large state space, they retain information with lower entropy.

so when we train a neural network made of quantum neurones (queurones), we want to increase correlation when the output vector agrees more with our goal, and decrease it when it disagrees.

so this just means generating more or less particle pairs with spin (for example), or observing one of the particles (to destroy the entanglement).

Hence we can build a very fast, high dimensionality neural net with only a few queurones, and we can also make sure that its operation cannot be observed without completely destroying its learning.

Thanks to Adria Gascon and Graham Cormode for discussion that led to this idea.