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相关概念视频

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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?
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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
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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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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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基于梯度的优化用于量子架构的搜索.

Zhimin He1, Jiachun Wei2, Chuangtao Chen3

  • 1School of Electronic and Information Engineering, Foshan University, Foshan, 528000, China.

Neural networks : the official journal of the International Neural Network Society
|July 14, 2024
PubMed
概括
此摘要是机器生成的。

我们介绍了基于梯度的量子架构搜索优化 (GQAS),这是设计量子电路的新方法. GQAS有效地搜索连续空间,优于变量量子算法的现有离散方法.

关键词:
量子架构的搜索架构是量子的架构.量子机器学习就是量子机器学习.自主监督学习学习变量量子算法是一种变量量子算法.变量量子电路是变量的.

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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
  • 算法设计 算法设计

背景情况:

  • 量子架构搜索 (QAS) 对于优化变量量子算法 (VQA) 中的量子电路至关重要.
  • 目前的QAS方法由于探索离散电路空间,因此效率低下.
  • 需要更高效,更可扩展的QAS技术.

研究的目的:

  • 提出基于梯度的量子架构搜索优化 (GQAS),这是量子电路设计的新方法.
  • 为了使量子电路架构在连续隐性空间中得到有效的探索.
  • 通过优化电路设计来提高变量量子算法的性能.

主要方法:

  • 开发了一个使用电路编码器,解码器和预测器的GQAS框架.
  • 使用编码器嵌入量子架构到连续的潜空间中.
  • 通过梯度下降优化潜伏表示,并使用解码器将其映射回离散架构.
  • 在一个大数据集的电路架构上使用自主监督学习 (SSL) 预训练编码器.

主要成果:

  • 与现有的差异化量子架构搜索 (DQAS) 方法相比,GQAS表现出卓越的性能.
  • 在变量量子Eigensolver (VQE) 上的模拟验证了拟议方法的有效性.
  • 持续的潜空间优化被证明比离散的搜索方法更有效.

结论:

  • GQAS提供了一种更有效和有效的方法来设计用于VQAs的量子电路.
  • 在连续潜伏空间中提出的基于梯度的方法代表了QAS的重大进步.
  • 这项工作为设计更复杂,更高性能量子电路铺平了道路.