Neural networks have transformed how machines find patterns in data, from recognizing faces in photos to predicting the shapes of proteins. So far, all of this progress has been made on ordinary classical computers, but with quantum computers now edging into practical use, there is a real possibility that neural networks could tap into distinctly quantum effects and operate in ways that classical machines never could. So far, however, neural networks have proven far more difficult to run on quantum hardware.

Through new research published in Physical Review Letters, Djamil Lakhdar-Hamina and colleagues at the University of Maryland, College Park, have built a neural network that runs on two different types of quantum computer, allowing them to test directly whether these systems can live up to their theoretical promise.

Elusive quantum advantage
A neural network is built from layers of simple units, each taking in signals and passing on an output depending on what it receives. To train a network, the connections between these units are adjusted until the network reliably produces the right answer for a given task.

In the quantum world, a similar structure can be built using qubits: the basic unit of quantum information, whose measurement outcomes stand in for the signals passed between layers. Researchers have long suspected that quantum versions of these networks could offer genuine advantages over classical ones, perhaps by exploiting quantum uncertainty. However, very few of these ideas have actually been tested on physical devices.