“We also need far fewer training iterations,” Kim says.
In exploratory work with South Korean car manufacturer Hyundai, Jungsang Kim at Duke University in Durham, North Carolina, and researchers at the firm IonQ in College Park, Maryland, developed quantum machine-learning algorithms that can tell the difference between ten road signs in laboratory tests (see go.nature.com/42tt7nr). Their quantum-based model used just 60 parameters to achieve the same accuracy as a classical neural network using 59,000 parameters. “We also need far fewer training iterations,” Kim says. “A model with 59,000 parameters requires at least 100,000 training data sets to train it. With quantum, your number of parameters is very small, so your training becomes extremely efficient as well.”