Google’s Tensorflow says “Hello Many Worlds”, as it now heads into Quantum Machine Learning

Tensorflow created a storm when it turned up on the Machine Learning scene a few years ago. There were some older frameworks such as Caffe for example. But Google altered the landscape when it unleashed Tensorflow on the world in 2015 and we have not looked back since. You must have heard of Machine Learning, but you may not have heard of Quantum Machine Learning – yes its a thing. The latest announcement from Google debuting its move into Quantum Machine Learning comes as no surprise.

One of the key benefits appears to be the easy integration of Google’s Quantum Framework cirq and tensorflow and keras. You can read the announcement from Google here in detail about the new developments and the white paper should you want more details.

Computational steps involved in the end-to-end pipeline for inference and training of a hybrid quantum-classical discriminative model for quantum data in TFQ

TensorFlow Quantum (TFQ) is a quantum machine learning library for rapid prototyping of hybrid quantum-classical ML models. Research in quantum algorithms and applications can leverage Google’s quantum computing frameworks, all from within TensorFlow.

library for hybrid quantum-classical machine learning (

Quantum Machine Learning Frameworks

Google is not the only way of creating QML models. Companies such as Xanadu with their PennyLane Framework have been pioneering Quantum Machine Learning. But we think the kick from Google will float all of the areas of classical

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