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相关概念视频

Network Function of a Circuit01:25

Network Function of a Circuit

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Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
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The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

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Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
249
State Space Representation01:27

State Space Representation

206
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
206
State Space to Transfer Function01:21

State Space to Transfer Function

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The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
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Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
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一个量子空间图在量子电路上的卷积神经网络模型.

Jin Zheng, Qing Gao, Maciej Ogorzalek

    IEEE transactions on neural networks and learning systems
    |April 15, 2024
    PubMed
    概括

    本研究介绍了一种量子空间图卷积神经网络 (QSGCN),用于处理量子电路上的复杂图数据. 量子神经网络 (QNN) 模型显示出有前途的学习和概括能力.

    科学领域:

    • 量子计算是一种量子计算.
    • 人工智能的人工智能
    • 图形神经网络 图形神经网络

    背景情况:

    • 非欧几里得数据处理是一个重大挑战.
    • 参数化量子电路 (PQC) 提供了新的计算范式.

    研究的目的:

    • 提出一个新的量子空间图卷积神经网络 (QSGCN) 模型.
    • 为了能够使用量子电路处理非欧几里德数据.

    主要方法:

    • 开发一个具有四个核心块的QSGCN模型:量子编码,量子图卷积层,量子图聚合层和网络优化.
    • 对模型可训练性的分析,包括荒高原现象.
    • 使用各种图形数据集进行模拟.

    主要成果:

    • 在图形数据上展示QSGCN模型的学习能力.
    • 验证模型的概括性和稳定性.
    • 在参数化量子电路平台上成功实现.

    结论:

    • 拟议的QSGCN模型是处理量子计算机上的非欧几里德数据的可行方法.
    • QSGCN模型表现出有效的学习,概括和稳定性.

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  • 对用于图形数据处理的QNN进行进一步研究是有必要的.