Machine learning with the quantum earthmover's distance

The quantum earthmover's distance is the quantum analogue of classical Wasserstein-1 distance, which generalizes the Hamming distance to general probability distributions/quantum states. This talk shows that the use of quantum earthmover's distance in machine learning can avoid the problem of barren landscapes in gradient descent methods. Applications are given to state and circuit learning.