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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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Programming Quantum Neural Networks on NISQ Systems: An Overview of Technologies and Methodologies.

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This survey explores high-level programming for Quantum Neural Networks (QNN) on Noisy Intermediate-Scale Quantum (NISQ) systems, detailing architectures, algorithms, and frameworks for quantum machine learning applications.

Keywords:
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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Noisy Intermediate-Scale Quantum (NISQ) systems enable quantum computing for Machine Learning (ML).
  • Quantum Neural Networks (QNN) are emerging as key tools for data analysis in quantum ML.
  • High-level programming interfaces are crucial for developing and utilizing QNN.

Purpose of the Study:

  • To survey state-of-the-art high-level programming approaches for Quantum Neural Network (QNN) development.
  • To provide a comprehensive overview of current QNN programming frameworks and techniques.

Main Methods:

  • Review of existing literature and frameworks for QNN programming.
  • Discussion of critical QNN algorithmic components, including hybrid workflows (Quantum Annealers, Parametrized Quantum Circuits), architectures, optimizers, and gradient calculations.
  • Analysis of target architectures and applications for QNN.

Main Results:

  • Identification and categorization of current high-level programming approaches for QNN.
  • Overview of software architecture and quantum simulators associated with QNN frameworks.
  • Discussion of key components and challenges in QNN development.

Conclusions:

  • High-level programming is essential for advancing QNN development and applications.
  • The survey provides a valuable resource for researchers and developers in the quantum machine learning field.
  • Future directions in QNN programming and framework development are highlighted.