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相关概念视频

Feedback control systems01:26

Feedback control systems

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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
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Feedback Loops01:01

Feedback Loops

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In most cases, excessive hormone production is prevented by negative feedback—a loop that starts with a stimulus inducing the release of a particular substance, like a hormone, to maintain a certain level before triggering a signal that results in a decrease in further release of the hormone.
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The Quantum-Mechanical Model of an Atom02:45

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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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Root Loci for Positive-Feedback Systems01:23

Root Loci for Positive-Feedback Systems

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The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees.
The construction rules for the root locus in positive feedback systems are similar to those in...
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

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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?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
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实现一个用于实时量子反的深度强化学习代理.

Kevin Reuer1,2, Jonas Landgraf3,4, Thomas Fösel3,4

  • 1Department of Physics, ETH Zurich, CH-8093, Zurich, Switzerland. kevin.reuer@phys.ethz.ch.

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

我们开发了一种新的,低延迟的强化学习代理,用于实时量子设备控制. 这种人工智能系统有效地初始化超导量子位,仅使用测量反.

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科学领域:

  • 量子技术是一种量子技术.
  • 人工智能的人工智能是人工智能.
  • 控制系统 控制系统

背景情况:

  • 精确的实时控制对于量子技术至关重要,需要比一致性时间更快的操作.
  • 无模型的强化学习 (RL) 提供了一种途径,可以在没有系统模型的情况下发现控制策略.
  • 为量子系统实施实时,反驱动的RL仍然是一个重大挑战.

研究的目的:

  • 实施强化学习代理来实时控制单个量子比特.
  • 为了证明该代理在高效初始化超导量子比特的能力.
  • 克服开发和培训RL代理人用于低延迟反系统的挑战.

主要方法:

  • 在现场可编程网关阵列 (FPGA) 上开发了一个微秒以下延迟的神经网络代理.
  • 利用无模型的强化学习来训练代理.
  • 用户基于测量的反用于代理培训和量子比特初始化.

主要成果:

  • 成功实施了用于单量子比特控制的实时增强学习代理.
  • 使用RL代理证明了超导量子位的高效初始化.
  • 在神经网络反循环中实现了微秒以下的延迟.

结论:

  • 这项工作为量子控制中实时,无模型的强化学习提供了一种可行的方法.
  • 实现的基于FPGA的代理方便了高效的超导量子比特初始化.
  • 这代表了整合RL用于控制量子设备和其他低延迟反系统的重要一步.