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相关概念视频

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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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Classification of Signals01:30

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Evolutionary Psychology01:20

Evolutionary Psychology

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Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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相关实验视频

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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EQNAS:进化量子神经架构 搜索图像分类

Yangyang Li1, Ruijiao Liu1, Xiaobin Hao1

  • 1Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xi'an 710071, China; International Research Center for Intelligent Perception and Computation, Xi'an 710071, China; Joint International Research Center for brain-like perception and cognition, Xi'an 710071, China; Collaborative Innovation Center of Quantum Information of Shaanxi Province, Xi'an 710071, China; School of Artificial Intelligence, Xidian University, Xi'an 710071, China.

Neural networks : the official journal of the International Neural Network Society
|October 8, 2023
PubMed
概括

本研究介绍了EQNAS,这是一种用于量子神经网络 (QNN) 的增强神经架构搜索方法. EQNAS提高了QNN分类的准确性,并减少了参数,解决了当前量子模型的局限性.

关键词:
神经架构搜索 (NAS) 是指神经架构搜索.量子电路中的量子电路.量子进化算法 (QEA) 是一个量子进化算法.量子神经网络 (QNN) 是一个量子神经网络.

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科学领域:

  • 量子计算是一种量子计算.
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 量子神经网络 (QNN) 为大量数据提供了速度和内存容量等优势.
  • 经典的神经网络在训练大规模数据集方面遇到了困难.
  • 目前的QNN受到复杂,人工设计的电路和低分类精度的影响.

研究的目的:

  • 提出一个有效的神经架构搜索方法,EQNAS,以提高QNN的性能.
  • 在电路复杂性和分类准确性方面克服手动QNN设计的局限性.

主要方法:

  • EQNAS采用一种进化方法:在图像量子编码后初始化量子群体.
  • 它涉及观察和评估人口的健康状况,然后使用量子旋转更新,电路构造和干扰交叉进行代更新.
  • 这个过程一直持续,直到达到令人满意的健康水平.

主要成果:

  • 实验证明了EQNAS算法的可行性和有效性.
  • 搜索QNN显示了与原始算法相比的显著改进.
  • 分类准确性在MNIST上增加了5.31%,在战舰数据集上增加了4.52%.
  • 实现了参数的减少:在MNIST上达到21.88%,在战舰数据集上达到31.25%.

结论:

  • EQNAS成功地增强了量子神经网络架构的搜索.
  • 该方法可以证明更好的分类准确性和更少的模型复杂性.
  • EQNAS代表了优化QNN用于实际应用的重大进步.