近期量子计算机中量子辅助机器学习的机遇和挑战
Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
摘要：
随着量子计算技术接近商业化和量子霸权时代，机器学习（ML）作为有前途的“杀手级”应用之一出现。尽管付出了巨大的努力，但大多数量子ML提案、ML从业者的需求以及近期量子设备在不久的将来展示量子增强的能力之间存在脱节。在这个贡献的重点收集“你会做什么与1000量子比特？“，我们提供了可以通过短期设备增强的棘手ML任务的具体示例。我们认为，为了实现这一目标，重点应该放在ML研究人员正在努力的领域，例如无监督和半监督学习中的生成模型，而不是流行的更易于处理的监督学习技术。我们还强调了具有潜在量子统计相关性的经典数据集的情况，其中量子模型可能更适合。我们专注于混合量子经典方法，并说明了一些我们预见的短期实现的关键挑战。最后，我们介绍了量子辅助亥姆霍兹机（QAHM），试图使用近期量子设备来处理连续变量的高维数据集。QAHM不像以前的方法那样使用量子计算机来辅助深度学习，而是使用深度学习来提取数据的低维二进制表示，适用于相对较小的量子处理器，可以帮助训练无监督生成模型。虽然我们在量子退火机上说明了这个概念，但其他量子平台也可以从这种混合量子-经典框架中受益。
英文摘要：
With quantum computing technologies nearing the era of commercialization and quantum supremacy, machine learning (ML) appears as one of the promising "killer" applications. Despite significant effort, there has been a disconnect between most quantum ML proposals, the needs of ML practitioners, and the capabilities of near-term quantum devices to demonstrate quantum enhancement in the near future. In this contribution to the focus collection on "What would you do with 1000 qubits?", we provide concrete examples of intractable ML tasks that could be enhanced with near-term devices. We argue that to reach this target, the focus should be on areas where ML researchers are struggling, such as generative models in unsupervised and semi-supervised learning, instead of the popular and more tractable supervised learning techniques. We also highlight the case of classical datasets with potential quantum-like statistical correlations where quantum models could be more suitable. We focus on hybrid quantum-classical approaches and illustrate some of the key challenges we foresee for near-term implementations. Finally, we introduce the quantum-assisted Helmholtz machine (QAHM), an attempt to use near-term quantum devices to tackle high-dimensional datasets of continuous variables. Instead of using quantum computers to assist deep learning, as previous approaches do, the QAHM uses deep learning to extract a low-dimensional binary representation of data, suitable for relatively small quantum processors which can assist the training of an unsupervised generative model. Although we illustrate this concept on a quantum annealer, other quantum platforms could benefit as well from this hybrid quantum-classical framework.
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