Google announces TensorFlow Quantum

TensorFlow Quantum

Google, in collaboration with the University of WaterlooX, and Volkswagen, announces the release of TensorFlow Quantum (TFQ), an open-source library for the rapid prototyping of quantum ML models.

TFQ provides the tools necessary for bringing the quantum computing and machine learning research communities together to control and model natural or artificial quantum systems; e.g. Noisy Intermediate Scale Quantum (NISQ) processors with ~50 – 100 qubits.

TFQ integrates Cirq with TensorFlow, and offers high-level abstractions for the design and implementation of both discriminative and generative quantum-classical models by providing quantum computing primitives compatible with existing TensorFlow APIs, along with high-performance quantum circuit simulators.

TFQ contains the basic structures, such as qubits, gates, circuits, and measurement operators that are required for specifying quantum computations. User-specified quantum computations can then be executed in simulation or on real hardware. Cirq also contains substantial machinery that helps users design efficient algorithms for NISQ machines, such as compilers and schedulers, and enables the implementation of hybrid quantum-classical algorithms to run on quantum circuit simulators, and eventually on quantum processors.

Google provides a review of these quantum applications in the TFQ white paper; each example can be run in-browser via Colab from a Github’s research repository. (Google)

Read more.