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相关概念视频

Area Computation by the Alternative Coordinate Method01:24

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The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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The first order operators using the del operator include the gradient, divergence and curl. Certain combinations of first order operators on a scalar or vector function yield second order expressions. Second-order expressions play a very important role in mathematics and physics. Some second order expressions include the divergence and curl of a gradient function, the divergence and curl of a curl function, and the gradient of a divergence function.
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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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非变量量子随机访问优化与交替操作员代替

Zichang He1, Rudy Raymond2, Ruslan Shaydulin2

  • 1Global Technology Applied Research, JPMorganChase, New York, NY, 10001, USA. zichang.he@jpmchase.com.

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概括
此摘要是机器生成的。

本研究介绍了一种非变量量子随机访问优化 (QRAO) 方法,使用量子交替运算符替代 (QAOA) 来解决优化问题. 固定的参数提高了量子计算机的可扩展性.

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科学领域:

  • 量子计算是一种量子计算.
  • 计算优化计算优化
  • 算法开发 算法开发

背景情况:

  • 量子计算机为复杂的优化问题提供了潜在的加速度.
  • 目前的量子方法由于硬件大小和错误纠正开销而面临限制.
  • 量子随机访问优化 (QRAO) 旨在减少量子资源需求.

研究的目的:

  • 为优化开发和基准测试一种非变量QRAO方法.
  • 为了克服可变QRAO方法的可扩展性问题.
  • 为了在近期量子设备上实现实用的QRAO执行.

主要方法:

  • 使用量子交替运算符替代 (QAOA) 实现了一个非变量的QRAO.
  • 将该方法应用于MaxCut优化问题.
  • 评估了固定的,独立于实例的参数,而不是变化的参数.
  • 评估各种QAOA设计选择 (混合器,初始状态,成本运算符实现).

主要成果:

  • 通过独立于实例的QAOA参数实现了良好的性能,消除了对变化训练的需求.
  • 在QRAO背景下确定了QAOA设计选择的有效策略.
  • 证明了QRAO的非变量方法的可行性.

结论:

  • 基于非变量QAOA的QRAO为量子优化提供了一个可扩展的解决方案.
  • 这种方法消除了实例特定参数训练的瓶.
  • 这些发现有助于QRAO在早期的容错量子计算机上的实际应用.