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.
翻译:我们引入了TensorFlow Quantum(TFQ),这是一个用于快速原型混合古典量子模型的开放源库,用于古典或量子数据。这个框架为在TensorFlow下设计和培训歧视性和基因化量子模型提供高层次的抽象概念,并支持高性能量子电路模拟器。我们通过几个实例对软件结构和构件进行概述,并审查混合量子古型神经网络理论。我们通过几个基本应用,包括量子分类、量子控制、模拟噪音量子电路和量子精度优化的监督学习,来说明TFQ的功能。此外,我们展示了如何应用TFQ来应对高级量子学习任务,包括元学习、分层学习、汉密尔顿学习、取样热状态、变异性量子粒子粒子、量子转换分类、基因化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界探索自然和人工量子系统模型以及最终发现有可能产生量子优势的新量子算算法的必要工具。