Google successfully combined its machine learning and quantum computing initiatives with the release of TensorFlow Quantum. The machine learning framework has the ability to build quantum data sets, quantum hybrid prototypes, and classical machine learning models, support quantum circuit simulators, and form discriminant and generative quantum models. According to a Google AI blog, TensorFlow Quantum can create quantum models with standard Keras functions and by providing quantum circuit simulators and quantum primitives that are compatible with existing TensorFlow APIs. The release of TensorFlow Quantum comes after Microsoft launches Azure Quantum and recent news that Honeywell is developing a quantum computer with a quantum volume of at least 64 that will be available in the next three months.
TensorFlow Quantum
In a summary of a paper by members of Google Unit X, the Institute for Quantum Computing at the University of Waterloo, the NASA Quantum AI Laboratory, Volkswagen, and Google Research, submitted to the arXiv preprint repository, the The authors explain what they believe TensorFlow Quantum can achieve, saying: "We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of natural and artificial quantum systems and ultimately discover new ones." quantum algorithms that could provide a quantum advantage". The article provides details on the TensorFlow Quantum software stack that combines the Cirq open source quantum circuit library with the TensorFlow machine learning platform. TensorFlow Quantum's ability to simulate properties is expected to lead to breakthroughs in the areas of life sciences, deciphering, development, and chemical or materials optimization. Via VentureBeat