Google today announced the launch of TensorFlow Quantum, bringing together machine learning and quantum computing initiatives at the company. The open-source library can prototype hybrid quantum and classic machine learning models, support quantum circuit simulators, and train discriminative and generative quantum models.

Last fall, Google said it achieved quantum supremacy last fall with the debut of a newly engineered solution, and follows the launch of Azure Qauntum and progress by companies like Honeywell.

Creating quantum models is made possible by providing quantum circuit simulators and quantum computing primitives compatible with existing TensorFlow APIs, according to a Google AI blog.

The framework is explained in a paper submitted March 6 to pre-print repository arXiv. The paper has more than 20 authors from Google’s X unit, The Institute for Quantum Computing at the University of Waterloo, NASA’s Quantum AI Lab, Volkswagen, and Google Research.

“We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage,” the paper reads.

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The paper details the TensorFlow Quantum software stack, which combines Cirq, an open-source quantum circuit library, and the TensorFlow machine learning platform.

Quantum computing enthusiasts hope the technology’s efficient simulating properties will lead to advances in life sciences, decryption, chemical or material development, or optimization.

Quantum machine learning combines machine learning algorithms that inherently rely on quantum properties for accelerated performance.

The launch of TensorFlow Quantum comes ahead of the TensorFlow Dev Summit, an annual meeting of machine learning practitioners who use the framework at Google offices in Silicon Valley. The in-person element of the event was cancelled due to continued fallout from the coronavirus.

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