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 QCS^TM 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.

在短期内寻找量子计算机的具体用例仍然是一个悬而未决的问题，机器学习通常被吹捧为将受到量子技术影响的首批领域之一。在这项工作中，我们研究并比较了量子模型与经典模型在机器学习中生成建模任务的能力。我们使用由相关货币对组成的真实世界金融数据集，并比较两个模型学习结果分布的能力——受限玻尔兹曼机和量子电路 Born 机。我们提供了大量的数值结果，表明模拟的波恩机在此任务中始终至少与玻尔兹曼机的性能相匹配，并且随着模型的扩展显示出卓越的性能。^TM平台，并且还能够在量子硬件上部分训练迄今为止最大的量子电路 Born 机器实例。最后，通过研究训练 Born 机器的纠缠能力，我们发现纠缠通常在问题实例中发挥作用，这证明了优于 Boltzmann 机器的优势。