TensorFlow Quantum: A Software Framework for Quantum Machine Learning

TLDR

TFQ is an open‑source library for rapid prototyping of hybrid quantum‑classical models for classical or quantum data. The framework provides high‑level abstractions for designing and training discriminative and generative quantum models within TensorFlow, supports high‑performance quantum circuit simulators, and is illustrated with examples and theory of hybrid quantum‑classical neural networks. TFQ is demonstrated through basic applications such as quantum classification, control, noisy circuit simulation, and quantum approximate optimization, and advanced tasks like meta‑learning, Hamiltonian learning, thermal state sampling, variational eigensolvers, phase transition classification, GANs, and reinforcement learning, with the goal of enabling new quantum algorithms that may yield a quantum advantage.

Abstract

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.