  Finding a concrete use case for quantum computers in the near term is still
an open question, with machine learning typically touted as one of the first
fields which will be impacted by quantum technologies. In this work, we
investigate and compare the capabilities of quantum versus classical models for
the task of generative modelling in machine learning. We use a real world
financial dataset consisting of correlated currency pairs and compare two
models in their ability to learn the resulting distribution - a restricted
Boltzmann machine, and a quantum circuit Born machine. We provide extensive
numerical results indicating that the simulated Born machine always at least
matches the performance of the Boltzmann machine in this task, and demonstrates
superior performance as the model scales. We perform experiments on both
simulated and physical quantum chips using the Rigetti forest platform, and
also are able to partially train the largest instance to date of a quantum
circuit Born machine on quantum hardware. Finally, by studying the entanglement
capacity of the training Born machines, we find that entanglement typically
plays a role in the problem instances which demonstrate an advantage over the
Boltzmann machine.

Quantum Physics;Machine Learning