Quantum computing and quantum technologies promise to further revolutionize our world. While in classical information theory the main unit of information is the bit, this is now replaced by the quantum bit or qubit. Many of the ideas from machine learning can be transferred to the quantum realm and be used efficiently in quantum computing. In this chapter we give a very brief introduction to quantum neural networks and show how they can be used in order to correct quantum errors that almost unavoidably take place in qubit networks that form quantum computers. We employ as an explicit example quantum autoencoders and use them in a 3-qubit error-correcting framework in order to correct logical qubit states affected by a bit-flip channel. Many important applications of this type will appear in the near future.