# A Quantum Hopfield Neural Network. (arXiv:1710.03599v2 [quant-ph] UPDATED)

Quantum computing allows for the potential of significant advancements in

both the speed and the capacity of widely-used machine learning techniques.

Here we employ quantum algorithms for the Hopfield network, which can be used

for pattern recognition, reconstruction, and optimization as a realization of a

content addressable memory system. We show that an exponentially large network

can be stored in a polynomial number of quantum bits by encoding the network

into the amplitudes of quantum states. By introducing a new classical technique

for operating the Hopfield network, we can leverage quantum algorithms to

obtain a quantum computational complexity that is logarithmic in the dimension

of the data. This potentially yields an exponential speed-up in comparison to

classical approaches. We also present an application of our method as a genetic

sequence recognizer.