Machine learning (ML), while it doesn’t exactly simulate systems in nature, has the ability to learn a model of a system and predict the system’s behavior. Over the past few years, classical ML models have shown promise in tackling challenging scientific issues, leading to advancements in image processing for cancer detection, forecasting earthquake aftershocks, predicting extreme weather patterns, and detecting new exoplanets. Posted by Alan Ho, Product Lead and Masoud Mohseni, Technical Lead, Google Research.
Google in collaboration with the University of Waterloo, X, and Volkswagen, announced the release of TensorFlow Quantum (TFQ), open-source library for the rapid prototyping of quantum ML models.
A quantum model has the ability to represent and generalize data with a quantum mechanical origin. However, to understand quantum models, two concepts must be introduced – quantum data and hybrid quantum-classical models.
TFQ allows researchers to construct quantum datasets, quantum models, and classical control parameters as tensors in a single computational graph. The outcome of quantum measurements, leading to classical probabilistic events, is obtained by TensorFlow Ops. Training can be done using standard Keras functions.
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