Tensor-Flow Quantum: An open source software framework for hybrid quantum-classical machine learning

We provide an overview of our progress on quantum-assisted and quantum-inspired algorithms for machine learning at Quantum AI Lab at Google. We present several new techniques for quantum circuit learning on Noisy Intermediate-Scale Quantum (NISQ) processors. In particular, 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 Cirq, and supports high-performance quantum circuit simulators such as qsim. We provide an overview of the software architecture and building blocks through several examples. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum state classification, quantum control, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning and layerwise learning. Specifically, we show how to train varying subsets of the quantum circuit's parameters iteratively while increasing the circuit depth to have sufficient representation of classical or quantum data.