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- W4366594995 endingPage "694" @default.
- W4366594995 startingPage "694" @default.
- W4366594995 abstract "Noisy Intermediate-Scale Quantum (NISQ) systems and associated programming interfaces make it possible to explore and investigate the design and development of quantum computing techniques for Machine Learning (ML) applications. Among the most recent quantum ML approaches, Quantum Neural Networks (QNN) emerged as an important tool for data analysis. With the QNN advent, higher-level programming interfaces for QNN have been developed. In this paper, we survey the current state-of-the-art high-level programming approaches for QNN development. We discuss target architectures, critical QNN algorithmic components, such as the hybrid workflow of Quantum Annealers and Parametrized Quantum Circuits, QNN architectures, optimizers, gradient calculations, and applications. Finally, we overview the existing programming QNN frameworks, their software architecture, and associated quantum simulators." @default.
- W4366594995 created "2023-04-23" @default.
- W4366594995 creator A5085178088 @default.
- W4366594995 date "2023-04-20" @default.
- W4366594995 modified "2023-10-01" @default.
- W4366594995 title "Programming Quantum Neural Networks on NISQ Systems: An Overview of Technologies and Methodologies" @default.
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- W4366594995 doi "https://doi.org/10.3390/e25040694" @default.
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