This week, Google announced a new version of its TensorFlow framework for building machine learning models named TensorFlow Quantum.
A feature of TensorFlow Quantum is the ability to simultaneously train and execute many quantum circuits.
This allows researchers to construct quantum datasets, quantum models, and classical control parameters as tensors in a single computational graph.
A quantum model has the ability to represent and generalize data with a quantum mechanical origin.
Quantum data exhibits superposition and entanglement, leading to joint probability distributions can be generated / simulated on quantum processors / sensors / networks include the simulation of chemicals and quantum matter, quantum control, quantum communication networks, quantum metrology, and much more.
This new feature has the ability to simulate a 32 qubit quantum circuit with a gate depth of 14 in 111 seconds on a single Google Cloud node.
Read more at the google blog.

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