Quantum machine learning has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. Some fundamental ideas behind quantum machine learning are similar to kernel methods in classical machine learning. Both process information by mapping it into high-dimensional vector spaces without explicitly calculating their numerical values. We explore a setup for performing classification on labeled classical datasets, consisting of a classical neural network connected to a quantum annealer. The neural network programs the quantum annealer's controls and thereby maps the annealer's initial states into new states in the Hilbert space. The neural network's parameters are optimized to maximize the distance of states corresponding to inputs from different classes and minimize the distance between quantum states corresponding to the same class. Recent literature showed that at least some of the "learning" is due to the quantum annealer, connecting a small linear network to a quantum annealer and using it to learn small and linearly inseparable datasets. In this study, we consider a similar but not quite the same case, where a classical fully-fledged neural network is connected with a small quantum annealer. In such a setting, the fully-fledged classical neural-network already has built-in nonlinearity and learning power, and can already handle the classification problem alone, we want to see whether an additional quantum layer could boost its performance. We simulate this system to learn several common datasets, including those for image and sound recognition. We conclude that adding a small quantum annealer does not provide a significant benefit over just using a regular (nonlinear) classical neural network.

翻译：量子机器学习有潜力推动人工智能发展，例如解决经典计算机难以处理的问题。其基础思想与经典机器学习中的核方法相似：两者都通过将信息映射到高维向量空间进行处理，而无需显式计算其数值。我们探索了一种对标记经典数据集进行分类的设置，该设置由连接至量子退火器的经典神经网络组成。神经网络对量子退火器的控制参数进行编程，从而将退火器的初始状态映射到希尔伯特空间中的新状态。网络参数通过优化，最大化对应不同类别输入的状态间距离，并最小化对应同一类别量子态之间的距离。近期文献表明，至少部分"学习"归功于量子退火器——将小型线性网络与量子退火器连接，用于学习小型线性不可分数据集。本研究考虑类似但略有不同的情况：将经典全功能神经网络与小型量子退火器连接。在此设定中，全功能经典神经网络已具备固有非线性和学习能力，可独立处理分类问题。我们旨在探究额外添加量子层是否能提升其性能。通过模拟系统学习多种常见数据集（包括图像和声音识别数据集），我们得出结论：相较于直接使用常规（非线性）经典神经网络，添加小型量子退火器并未带来显著优势。