This news report was 1st published on our sister Site, The Internet Of All Things.
Google, in partnership with the University of Waterloo, X, and Volkswagen, has announced the release of the open-source TensorFlow Quantum (TFQ) library. The aim is to initiate rapid prototyping of quantum ML models.
TFQ, announced Google on its official blog, will provide 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.
It was only last fall that Google had claimed “quantum computing supremacy” with the debut of an engineered solution. That had led to a controversy since other Internet companies had contested Google’s claim.
Under the hood, the Google Tensorflow Quantum 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.
What is a Quantum ML Model?
A quantum model has the ability to represent and generalise data with a quantum mechanical origin. However, to understand quantum models, two concepts must be introduced – quantum data and hybrid quantum-classical models.
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Image credit: Google
