TensorFlow Quantum：一个量子机器学习的软件框架
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
摘要：
我们介绍TensorFlow Quantum（TFQ），这是一个开源库，用于经典或量子数据的混合量子-经典模型的快速原型制作。该框架为TensorFlow下的判别和生成量子模型的设计和训练提供了高级抽象，并支持高性能量子线路模拟器。我们提供了一个概述的软件架构和构建模块，通过几个例子，并回顾混合量子经典神经网络的理论。我们说明TFQ功能通过几个基本的应用程序，包括监督学习的量子分类，量子控制，模拟嘈杂的量子线路，量子近似优化。此外，我们展示了如何应用TFQ来解决高级量子学习任务，包括元学习，分层学习，哈密顿学习，采样热态，变分量子本征求解器，量子相变分类，生成对抗网络和强化学习。我们希望这个框架为量子计算和机器学习研究社区提供必要的工具，以探索自然和人工量子系统的模型，并最终发现可能产生量子优势的新量子算法。
英文摘要：
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, simulating noisy quantum circuits, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, layerwise learning, Hamiltonian learning, sampling thermal states, variational quantum eigensolvers, classification of quantum phase transitions, generative adversarial networks, and reinforcement learning. We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage.
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。
