Generating high-quality data (e.g., images or video) is one of the most exciting and challenging frontiers in unsupervised machine learning. Utilizing quantum computers in such tasks to potentially enhance conventional machine-learning algorithms has emerged as a promising application but poses big challenges due to the limited number of qubits and the level of gate noise in available devices. In this work, we provide the first practical and experimental implementation of a quantum-classical generative algorithm capable of generating high-resolution images of handwritten digits with state-of-the-art gate-based quantum computers. In our quantum-assisted machine-learning framework, we implement a quantum-circuit-based generative model to learn and sample the prior distribution of a generative adversarial network. We introduce a multibasis technique which leverages the unique possibility of measuring quantum states in different bases, hence enhancing the expressivity of the prior distribution. We train this hybrid algorithm on an ion-trap device based on ${}^{171}{\mathrm{Yb}}^{+}$ ion qubits to generate high-quality images and quantitatively outperform comparable classical generative adversarial networks trained on the popular MNIST dataset for handwritten digits.

中文翻译：

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用离子阱量子计算机生成高分辨率手写数字

生成高质量数据（例如，图像或视频）是无监督机器学习中最令人兴奋和最具挑战性的前沿之一。在此类任务中利用量子计算机来潜在地增强传统的机器学习算法已成为一种很有前途的应用，但由于可用设备中量子比特的数量和门噪声水平有限，这带来了巨大的挑战。在这项工作中，我们提供了量子经典生成算法的第一个实际和实验实现，该算法能够使用最先进的基于门的量子计算机生成手写数字的高分辨率图像。在我们的量子辅助机器学习框架中，我们实现了一个基于量子电路的生成模型来学习和采样生成对抗网络的先验分布。我们引入了一种多基技术，该技术利用了在不同基中测量量子态的独特可能性，从而增强了先验分布的表达能力。我们在离子阱设备上训练这种混合算法，基于${}^{171}{镱}^{+}$离子量子比特生成高质量图像并在数量上优于在流行的 MNIST 数据集上训练的手写数字的可比经典生成对抗网络。