Joint Quantum Institute, Department of Physics, and Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD 20742, USA.
Department of Computer Science, University College London, WC1E 6BT London, UK.
Sci Adv. 2019 Oct 18;5(10):eaaw9918. doi: 10.1126/sciadv.aaw9918. eCollection 2019 Oct.
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. Here, we implement a data-driven quantum circuit training algorithm on the canonical Bars-and-Stripes dataset 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.
生成模型是机器学习的一个分支，应用范围从计算机视觉到化学设计。预计它将是最适合利用近期量子计算机提供的额外资源的技术之一。在这里，我们使用量子-经典混合机器在典型的 Bars-and-Stripes 数据集上实现了数据驱动的量子电路训练算法。训练过程是在囚禁离子量子计算机上运行参数化电路，并将结果输入到经典优化器中。我们将两种不同的策略，粒子群优化和贝叶斯优化，应用于这个任务。我们表明，量子电路对目标分布的收敛性在很大程度上取决于量子硬件和经典优化策略。我们的研究代表了首次成功训练高维通用量子电路，突出了混合学习方案的前景和挑战。
