量子辅助亥姆霍兹机器：近期设备中工业数据集的量子经典深度学习框架
Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices
摘要：
机器学习被认为是近期量子技术的关键应用之一，因为它具有很高的商业价值和广泛的适用性。在这项工作中，我们引入了\textit{quantum-assisted Helmholtz machine:}，这是一个混合量子经典框架，具有处理连续变量上的高维真实世界机器学习数据集的潜力。与之前的方法所建议的仅使用量子计算机来辅助深度学习不同，我们使用深度学习来提取数据的低维二进制表示，适合在相对较小的量子计算机上处理。然后，量子硬件和深度学习架构协同工作，训练一个无监督的生成模型。我们使用D-Wave 2000 Q量子设备的1644个量子比特来演示这个概念，以模拟具有16 x16连续值像素的MNIST手写数字数据集的子采样版本。虽然我们在量子退火机上说明了这一概念，但可以在这个灵活的框架内探索对其他量子平台的适应，例如离子阱技术或超导门模型架构。
英文摘要：
Machine learning has been presented as one of the key applications for near-term quantum technologies, given its high commercial value and wide range of applicability. In this work, we introduce the \textit{quantum-assisted Helmholtz machine:} a hybrid quantum-classical framework with the potential of tackling high-dimensional real-world machine learning datasets on continuous variables. Instead of using quantum computers only to assist deep learning, as previous approaches have suggested, we use deep learning to extract a low-dimensional binary representation of data, suitable for processing on relatively small quantum computers. Then, the quantum hardware and deep learning architecture work together to train an unsupervised generative model. We demonstrate this concept using 1644 quantum bits of a D-Wave 2000Q quantum device to model a sub-sampled version of the MNIST handwritten digit dataset with 16x16 continuous valued pixels. Although we illustrate this concept on a quantum annealer, adaptations to other quantum platforms, such as ion-trap technologies or superconducting gate-model architectures, could be explored within this flexible framework.
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