School of Computing Science, University of Glasgow, Glasgow, G12 8QQ, UK.
OxBrdgRbtx Ltd, Stratford-upon-Avon, CV37 6XU, UK.
Sci Rep. 2023 Mar 9;13(1):3939. doi: 10.1038/s41598-023-30910-7.
We demonstrate the feasibility of framing a classically learned deep neural network as an energy based model that can be processed on a one-step quantum annealer in order to exploit fast sampling times. We propose approaches to overcome two hurdles for high resolution image classification on a quantum processing unit (QPU): the required number and the binary nature of the model states. With this novel method we successfully transfer a pretrained convolutional neural network to the QPU. By taking advantage of the strengths of quantum annealing, we show the potential for classification speedup of at least one order of magnitude.
我们证明了将经典学习的深度神经网络构建为基于能量的模型的可行性，该模型可以在一步量子退火机上进行处理，以利用快速采样时间。我们提出了一些方法来克服在量子处理单元 (QPU) 上进行高分辨率图像分类的两个障碍：所需的模型状态数量和二进制性质。通过这种新方法，我们成功地将预训练的卷积神经网络转移到 QPU 上。利用量子退火的优势，我们展示了分类速度至少提高一个数量级的潜力。
