Generative modeling is a flavor of machine learning with applications ranging
from computer vision to chemical design. It is expected to be one of the
techniques most suited to take advantage of the additional resources provided
by near-term quantum computers. We implement a data-driven quantum circuit
training algorithm on the canonical Bars-and-Stripes data set using a
quantum-classical hybrid machine. The training proceeds by running
parameterized circuits on a trapped ion quantum computer, and feeding the
results to a classical optimizer. We apply two separate strategies, Particle
Swarm and Bayesian optimization to this task. We show that the convergence of
the quantum circuit to the target distribution depends critically on both the
quantum hardware and classical optimization strategy. Our study represents the
first successful training of a high-dimensional universal quantum circuit, and
highlights the promise and challenges associated with hybrid learning schemes.

这篇论文研究了使用基于数据的方法在陷阱离子量子计算机上训练量子电路，以实现一种高维普适量子电路的学习，为混合学习方案提供了进一步的发展和挑战。