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, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, Hamiltonian learning, and sampling thermal states. 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.

中文翻译：

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TensorFlow Quantum：用于量子机器学习的软件框架

我们介绍了 TensorFlow Quantum (TFQ)，这是一个开源库，用于对经典或量子数据的混合量子经典模型进行快速原型设计。该框架为 TensorFlow 下判别式和生成式量子模型的设计和训练提供了高级抽象，并支持高性能量子电路模拟器。我们通过几个示例概述了软件架构和构建块，并回顾了混合量子经典神经网络的理论。我们通过几个基本应用来说明 TFQ 功能，包括用于量子分类、量子控制和量子近似优化的监督学习。此外，我们展示了如何应用 TFQ 来处理高级量子学习任务，包括元学习、哈密顿学习、和采样热状态。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来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。