without the programmer necessarily detailing how to do it. Novel techniques for training networks of many layers (deep networks) is credited with giving impetus to the neural networks approach [@LecunBengioHinton15].

The field of quantum machine learning is rapidly developing though the focus has aruably not been on the connection to neural networks. Quantum machine learning, see e.g. [@LloydMR13; @LloydMR13ii; @Montanaro15; @Aaronson15; @GarneroneZL12; @HarrowHL09; @LloydGZ16; @RebenstrostML13; @WiebeBL12; @Adcock15; @HeimRIT15; @GrossYFBE10; @Dunjko16; @Wittek14] employs quantum information processing (QIP) [@NielsenChuang00]. QIP uses quantum superpositions of states with the aim of faster processing of classical data as well as tractable simulation of quantum systems. In a superposition each bit string is associated with two numbers: the probability of the string and the [*phase*]{} [@GarnerDNMV15], respectively. The phase impacts the future probabilities via a time evolution law. There are certain promising results that concern quantum versions of recurrent neural networks, wherein neurons talk to each other in all directions rather than feeding signals forward to the next layer, e.g. with the purpose of implementing quantum simulated annealing [@Lechnere1500838; @quantum-deep-learning; @HeimRIT15; @GarneroneZL12]. In [@Schuld14] several papers proposing quantum neural network designs are discussed and critically reviewed. A key challenge to overcome is the clash between the 