使用混合 CPU-FPGA 方法的量子 AI 模拟器
Quantum AI simulator using a hybrid CPU-FPGA approach
AI总结：
针对含噪量子计算机物理比特受限的问题，提出一种用于图像分类的异构 CPU-FPGA 量子核模拟器，实现了比传统 CPU 快 470 倍的量子核估计，最高支持 780 维特征，并在 Fashion-MNIST 数据集上表现出与优化高斯核相当的分类性能。
摘要：
量子核方法在量子机器学习领域引起了广泛关注。然而，当前含噪量子计算机拥有的物理量子比特数量，阻碍了在更现实环境中探索量子核的适用性，从而限制了为量子核编码的特征数量。因此，需要利用经典技术开发一种高效的、特定应用的量子计算模拟器。在此，我们专注于为图像分类经验性设计的量子核，并展示了现场可编程门阵列（FPGA）的实现。我们表明，我们异构 CPU-FPGA 计算的量子核估计比传统 CPU 实现快 470 倍。我们的特定应用量子核及其高效 FPGA 实现的协同设计，使我们能够就特征而言执行最大规模的基于门的量子核数值模拟之一，最高达 780 维特征。我们使用 Fashion-MNIST 数据集将我们的量子核应用于分类任务，并表明我们的量子核与优化超参数的高斯核相当。
英文摘要：
The quantum kernel method has attracted considerable attention in the field of quantum machine learning. However, exploring the applicability of quantum kernels in more realistic settings has been hindered by the number of physical qubits current noisy quantum computers have, thereby limiting the number of features encoded for quantum kernels. Hence, there is a need for an efficient, application-specific simulator for quantum computing by using classical technology. Here we focus on quantum kernels empirically designed for image classification and demonstrate a field programmable gate arrays (FPGA) implementation. We show that the quantum kernel estimation by our heterogeneous CPU-FPGA computing is 470 times faster than that by a conventional CPU implementation. The co-design of our application-specific quantum kernel and its efficient FPGA implementation enabled us to perform one of the largest numerical simulations of a gate-based quantum kernel in terms of features, up to 780-dimensional features. We apply our quantum kernel to classification tasks using Fashion-MNIST dataset and show that our quantum kernel is comparable to Gaussian kernels with the optimized hyperparameter.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
