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What role does TensorFlow Quantum (TFQ) play in enabling machine learning over parameterized quantum circuits, and how does it support the development of hybrid quantum-classical models?

Tuesday, 11 June 2024 by EITCA Academy

TensorFlow Quantum (TFQ) is an advanced software framework designed to facilitate the integration of quantum computing paradigms with classical machine learning models. The primary role of TFQ lies in its ability to enable machine learning over parameterized quantum circuits (PQCs) and to support the development of hybrid quantum-classical models. This integration is important for harnessing

Published in Artificial Intelligence, EITC/AI/TFQML TensorFlow Quantum Machine Learning, Overview of TensorFlow Quantum, TensorFlow Quantum: a software platform for hybrid quantum-classical ML, Examination review

Tagged under: Artificial Intelligence, Hybrid Models, Machine Learning, PQCs, Quantum Algorithms, Quantum Circuits, Quantum Computing, TensorFlow

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What role does TensorFlow Quantum (TFQ) play in enabling machine learning over parameterized quantum circuits, and how does it support the development of hybrid quantum-classical models?

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What role does TensorFlow Quantum (TFQ) play in enabling machine learning over parameterized quantum circuits, and how does it support the development of hybrid quantum-classical models?

Eligibility for EITCA Academy 80% EITCI DSJC Subsidy support