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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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Superconductor01:24

Superconductor

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A substance that reaches superconductivity, a state in which magnetic fields cannot penetrate, and there is no electrical resistance, is referred to as a superconductor. In 1911, Heike Kamerlingh Onnes of Leiden University, a Dutch physicist, observed a relation between the temperature and the resistance of the element mercury. The mercury sample was then cooled in liquid helium to study the linear dependence of resistance on temperature. It was observed that, as the temperature decreased, the...
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Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

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A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
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Types Of Superconductors01:28

Types Of Superconductors

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A superconductor is a substance that offers zero resistance to the electric current when it drops below a critical temperature. Zero resistance is not the only interesting phenomenon as materials reach their transition temperatures. A second effect is the exclusion of magnetic fields. This is known as the Meissner effect. A light, permanent magnet placed over a superconducting sample will levitate in a stable position above the superconductor. High-speed trains that levitate on strong...
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Spinal Cord: Information Processing01:10

Spinal Cord: Information Processing

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The spinal cord is an integral hub for motor and sensory information that enables the brain to communicate with the peripheral nervous system (PNS). This communication consists of relaying sensory data and transmission of motor commands.
Sensory Information Processing
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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在超导处理器上的深度量子神经网络.

Xiaoxuan Pan1, Zhide Lu1, Weiting Wang1

  • 1Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China.

Nature communications
|July 6, 2023
PubMed
概括
此摘要是机器生成的。

研究人员演示了在超导处理器上使用反向传播训练深度量子神经网络. 这种量子机器学习的进步有效地学习量子通道和分子能量,指导未来的量子设备应用.

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科学领域:

  • 量子计算是一种量子计算.
  • 机器学习 机器学习
  • 量子机器学习就是量子机器学习.

背景情况:

  • 深度学习和量子计算最近取得了重大进展.
  • 这些领域的交集创造了量子机器学习 (QML) 的新兴领域.

研究的目的:

  • 用反向传播算法实验证明深度量子神经网络 (DQNN) 的训练.
  • 评估DQNNs在学习量子任务中的效率和真实性.

主要方法:

  • 使用一个六量子比特可编程超导处理器进行实验演示.
  • 在实验中进行了反向传播的前向传递.
  • 经典模拟了反向传播算法的反向传递.

主要成果:

  • 成功训练了三层DQNN来学习两量子比特量子通道,平均保真率高达96.0%.
  • 在学习分子的基本状态能量方面取得了93.3%的准确性.
  • 训练有素的六层DQNN用于高达94.8%的平均保真度的单量子比特量子通道.

结论:

  • 需要的连贯量子比特的数量不会随着DQNN深度而增加.
  • 这一发现为当前和未来量子硬件上的QML应用提供了宝贵的指导.