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.

通过使用混合量子 - 经典方法并介绍了一个名为 Quantum-assisted Helmholtz Machine (QAHM) 的新算法，我们提出了一种解决当前量子计算和量子机器学习的困难关系，特别是在生成模型方面的解决方案。