论文标题
启用了载有量子的RBM优势：用于量子图像压缩和生成学习的卷积自动编码器
A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning
论文作者
论文摘要
了解如何将D-Wave量子计算机用于机器学习问题是越来越兴趣的。我们的工作评估了使用D波作为机器学习的采样器的可行性。我们描述了一种混合系统，该系统将经典的深神经网络自动编码器与使用D波的量子退火限制的玻璃体机（RBM）相结合。我们使用两个数据集（MNIST数据集和MNIST时尚数据集）评估了混合自动编码器算法。我们通过使用基于量子RBM生成的样品的下游分类方法来评估该方法的质量。我们的方法克服了当前2000 Qubit D-Wave处理器中的两个关键局限性，即用于适应完全连接的量子目标功能的典型问题大小的量子数量有限，以及二进制像素表示的样本。由于这些局限性，我们能够展示如何实现灰度28 x 28尺寸的图像的近22倍压缩因子，以二进制6 x 6尺寸的图像，而原始28 x 28灰度图像的恢复有损。我们进一步展示了在训练RBM后从D波中生成样品，导致28 x 28张图像是原始输入数据分布的变化，而不是重新创建培训样品。我们使用深层卷积神经网络提出了MNIST分类问题，该网络使用量子RBM的样品训练MNIST分类器，并将结果与使用原始MNIST训练数据集训练的MNIST分类器进行了比较，以及使用经典RBM样品培训的MNIST分类器。我们的混合自动编码器方法指示了RBM结果相对于使用当前的RBM经典计算机实现用于基于图像的机器学习的优势，以及下一代D-WAVE量子系统的更有希望的结果。
Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work evaluates the feasibility of using the D-Wave as a sampler for machine learning. We describe a hybrid system that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave. We evaluate our hybrid autoencoder algorithm using two datasets, the MNIST dataset and MNIST Fashion dataset. We evaluate the quality of this method by using a downstream classification method where the training is based on quantum RBM-generated samples. Our method overcomes two key limitations in the current 2000-qubit D-Wave processor, namely the limited number of qubits available to accommodate typical problem sizes for fully connected quantum objective functions and samples that are binary pixel representations. As a consequence of these limitations we are able to show how we achieved nearly a 22-fold compression factor of grayscale 28 x 28 sized images to binary 6 x 6 sized images with a lossy recovery of the original 28 x 28 grayscale images. We further show how generating samples from the D-Wave after training the RBM, resulted in 28 x 28 images that were variations of the original input data distribution, as opposed to recreating the training samples. We formulated an MNIST classification problem using a deep convolutional neural network that used samples from a quantum RBM to train the MNIST classifier and compared the results with an MNIST classifier trained with the original MNIST training data set, as well as an MNIST classifier trained using classical RBM samples. Our hybrid autoencoder approach indicates advantage for RBM results relative to the use of a current RBM classical computer implementation for image-based machine learning and even more promising results for the next generation D-Wave quantum system.
