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相关概念视频

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

125
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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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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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
446
Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
210
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

95
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
95
Introduction to Learning01:18

Introduction to Learning

472
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
472

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Gradient Echo Quantum Memory in Warm Atomic Vapor
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量子循环神经网络用于顺序学习.

Yanan Li1, Zhimin Wang1, Rongbing Han1

  • 1Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China.

Neural networks : the official journal of the International Neural Network Society
|July 24, 2023
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种新的量子循环神经网络 (QRNN),用于顺序学习. 这种硬件效率高的QRNN模型在杂的中级量子 (NISQ) 设备上表现出卓越的性能,性能优于经典模型.

关键词:
气象学指标 气象学指标量子深度神经网络是一个量子深度神经网络.量子循环神经网络是一个量子循环神经网络.股票价格 股票价格 股票价格时间序列数据时间序列数据.文本分类的分类方法

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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:

  • 开发正规的量子反复神经网络 (QRNN) 模型对于推进量子深度学习至关重要.
  • 噪音中等尺度量子 (NISQ) 设备为特定的计算任务提供了潜在的优势.
  • 现有的QRNN模型在NISQ设备上的硬件要求和可访问性方面存在局限性.

研究的目的:

  • 提出一种新的,硬件效率高的量子循环神经网络 (QRNN) 模型.
  • 建立一个适合近期量子器件的正规QRNN模型.
  • 证明拟议的QRNN的实际适用性和卓越性能.

主要方法:

  • 构建硬件效率高的量子循环块 (QRB).
  • QRB 的分层堆叠,以减少量子设备的一致性时间要求.
  • 验证使用不同的经典序列数据集:气象指标,股票价格和文本分类.

主要成果:

  • 与经典的反复神经网络 (RNN) 和其他量子神经网络 (QNN) 模型相比,拟议的QRNN模型显示出明显改善的预测和分类准确性.
  • 该QRNN有效地捕获和预测时间序列数据中的复杂细节.
  • 该模型的实用电路结构和减少的连贯时间要求使其对NISQ设备具有高度可访问性.

结论:

  • 开发的QRNN是正规QRNN模型的强有力的候选者.
  • QRNN模型为实现近期应用中的量子优势提供了一个有希望的途径.
  • 这项研究促进了量子深度学习在当前量子硬件上的实际实施.