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相关概念视频

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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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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Propagation of Uncertainty from Systematic Error01:10

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
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Maxwell-Boltzmann Distribution: Problem Solving01:20

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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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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
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Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
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使用量子真空噪声的光子概率机器学习.

Seou Choi1, Yannick Salamin2,3, Charles Roques-Carmes4,5

  • 1Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA. seouc130@mit.edu.

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概括
此摘要是机器生成的。

研究人员开发了一台使用量子真空噪声进行机器学习的光子概率计算机. 这种新的硬件能够实现高速,节能的概率推断和图像生成,为先进的AI应用铺平了道路.

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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
  • 光子学是指光子学的使用方法.

背景情况:

  • 概率机器学习依赖于随机性来编码不确定性.
  • 量子真空噪声提供了高速,节能随机性的来源.
  • 有限的光子硬件存在,用于控制概率机器学习中的随机元素.

研究的目的:

  • 使用新型光子概率神经元 (PPN) 实现光子概率计算机.
  • 证明PPN在解决概率机器学习任务方面的能力.
  • 为可扩展,超快速和节能全光学概率计算提出一条途径.

主要方法:

  • 使用可视化光学参数振荡器 (OPO) 实现PPN,使用真空级注入偏差场.
  • 用电子处理器 (FPGA或GPU) 编程时间复合PPN的测量和反循环.
  • 利用量子真空噪声作为一种随机种子来编码不确定性并生成样本.

主要成果:

  • 在MNIST手写数字上成功演示了概率推理和图像生成.
  • 使用量子真空噪声编码分类不确定性和概率样本生成.
  • 提出了一个全光学概率计算平台,采样速率为~1 Gbps,能源消耗为~5 fJ/MAC.

结论:

  • 开发的光子概率计算机为机器学习提供了可扩展,超快,节能的硬件解决方案.
  • 这项工作推动了量子现象与人工智能的集成,用于下一代计算.
  • 拟议的全光学平台为概率计算带来了速度和能源效率的显著改善.