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相关概念视频

Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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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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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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When an object is in equilibrium, it is either at rest or moving with a constant velocity. There are two types of equilibrium: static and dynamic. Static equilibrium occurs when an object is at rest, while dynamic equilibrium occurs when an object is moving with a constant velocity. In both cases, there must be a balance of forces acting on the object.
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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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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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量子增强的马尔科夫链蒙特卡洛

David Layden1, Guglielmo Mazzola2,3, Ryan V Mishmash4,5

  • 1IBM Quantum, Almaden Research Center, San Jose, CA, USA. david.layden@ibm.com.

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概括

本研究介绍了马尔科夫链蒙特卡洛 (MCMC) 采样的量子算法. 它比经典方法更快地纠正分布,为机器学习和物理提供了潜在的加速.

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科学领域:

  • 量子计算
  • 计算物理
  • 机器学习

背景情况:

  • 目前的量子处理器面临大小和错误率的限制.
  • 短期量子算法通常集中在复杂的概率分布的采样上.
  • 马尔科夫链蒙特卡洛 (MCMC) 是从分布中采样的一个关键技术.

研究的目的:

  • 介绍并演示经典伊辛模型的波兹曼分布采样的量子算法.
  • 解决目前量子硬件可以解决的有用采样问题的需求.
  • 为MCMC提供一种可证明的量子方法.

主要方法:

  • 开发了一种实现马尔科夫链蒙特卡洛 (MCMC) 的量子算法.
  • 在当前量子硬件上实验证明了算法.
  • 通过实验和经典模拟分析了融合率.

主要成果:

  • 量子MCMC算法在较少的代过程中显示了趋同.
  • 实验表明量子算法是强大的噪音.
  • 模拟显示了古典MCMC方法的立方到四方多项式加速度.

结论:

  • 开发的量子MCMC算法为解决有用的抽样问题提供了可行的途径.
  • 经验加速表明有可能缓解机器学习,统计物理和优化中的计算瓶.
  • 这项工作为量子计算机解决实际采样挑战打开了道路.