论文标题
与各种张量 - 网络量子电路的图像分类的实用概述
A practical overview of image classification with variational tensor-network quantum circuits
论文作者
论文摘要
量子机学习的电路设计仍然是一个巨大的挑战。受张量网络在不同领域的应用及其在古典机器学习环境中的新颖性的启发，一种设计变异电路的方法是将电路架构基于张量网络。在这里，我们全面描述了张量 - 网络量子电路以及如何在模拟中实现它们。这包括利用电路切割，这是一种用于评估比当前量子设备上可用的电路的技术。然后，我们通过使用PennyLane模拟各种张量 - 网络量子电路来说明计算要求和可能的应用，Pennylane是一个用于量子计算机差速器编程的开源Python库。最后，我们演示了如何将这些电路应用于日益复杂的图像处理任务，完成了一种灵活的方法，以设计可应用于工业相关的机器学习任务的电路。
Circuit design for quantum machine learning remains a formidable challenge. Inspired by the applications of tensor networks across different fields and their novel presence in the classical machine learning context, one proposed method to design variational circuits is to base the circuit architecture on tensor networks. Here, we comprehensively describe tensor-network quantum circuits and how to implement them in simulations. This includes leveraging circuit cutting, a technique used to evaluate circuits with more qubits than those available on current quantum devices. We then illustrate the computational requirements and possible applications by simulating various tensor-network quantum circuits with PennyLane, an open-source python library for differential programming of quantum computers. Finally, we demonstrate how to apply these circuits to increasingly complex image processing tasks, completing this overview of a flexible method to design circuits that can be applied to industrially-relevant machine learning tasks.
