Google unveils TensorFlow Quantum, an open-source library for accelerated prototyping models for Machine Learning (ML) quantum. The structure can develop prototype hybrid, quantum data sets, and classic quantum models of machine learning quantum circuit simulators and train discriminative and generative quantum models.
Google building quantum models are more feasible with regular Keras functions and by storing quantum circuit simulators and quantum computing compatible with existing TensorFlow APIs, in addition to circuit simulators high-performance quantum. The structure was developed in collaboration with the University of Waterloo, NASA's Quantum AI Lab, Google X unit and Volkswagen.
"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 algorithm which could potentially yield a quantum advantage." In the future, we hope to expand the range of custom simulation hardware supported to include GPU and TPU integration," the paper reads.
As explained by Google, TFO integrates Cirq with TensorFlow, an open source quantum circuit library and the TensorFlow ML platform. Google post announced, "We believe that the bridge between the ML and Quantum communities will lead to exciting new discoveries and accelerate the discovery of new quantum algorithms to solve the world's most challenging problems."
TensorFlow Quantum, the launch comes in the same week as the TensorFlow Dev Summit, an annual meeting of Machine Learning professionals who use the structure at Google offices in Silicon Valley. Due to the strain on the coronavirus, the event was canceled.