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相关概念视频

Quantitative Analysis01:12

Quantitative Analysis

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Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of 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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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Norton's Theorem01:14

Norton's Theorem

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Norton's theorem is a fundamental principle stating that a linear two-terminal circuit can be substituted with an equivalent circuit, which comprises a current source (ⅠN) in parallel with a resistor (RN). Here, ⅠN represents the short-circuit current flowing through the terminals, and RN stands for the input or equivalent resistance at the terminals when all independent sources are deactivated. This implies that the circuit illustrated in Figure (a) can be exchanged with the...
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Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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相关实验视频

Updated: Jun 3, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Published on: September 8, 2023

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量子计算在社区中用于反欺诈应用的检测.

Yanbo Justin Wang1, Xuan Yang1, Chao Ju2

  • 1Longying Zhida (Beijing) Technology Co., Ltd., Beijing 100020, China.

Entropy (Basel, Switzerland)
|January 8, 2025
PubMed
概括

本研究介绍了一种使用交易网络中社区检测的量子计算欺诈检测方法. 量子方法比经典方法更快,更有效,识别了具有最多欺诈账户的高风险社区.

关键词:
卢温 (Louvain) 是一个城市.这是反欺诈措施.连贯化机 (CIM) 是一种连贯化机.社区检测 社区检测四位数的不受约束的二进制优化 (QUBO)量子计算是一种量子计算.模拟火的模拟火

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科学领域:

  • 金融安全 金融安全
  • 网络科学 网络科学
  • 量子计算是一种量子计算.

背景情况:

  • 大数据需要先进的欺诈检测,以确保财务安全.
  • 交易数据可以作为网络建模,以识别可疑模式.

研究的目的:

  • 开发一种使用量子计算用于交易网络中社区检测的新型欺诈检测方法.
  • 为了评估量子计算的效率和有效性与经典算法对抗这一任务.

主要方法:

  • 交易数据以图形的形式建模,账户作为节点,交易作为边缘.
  • 社区检测优化使用正方位不受约束的二进制优化 (QUBO) 模型.
  • QUBO 通过 Coherent Ising Machine (CIM) 解决,用于社区识别和风险评估.

主要成果:

  • 通过使用CIM.成功将308个节点分为四个社区.
  • 与卢瓦恩和模拟化 (SA) 算法相比,CIM展示了更快的计算时间.
  • 通过模块化函数来衡量,实现了优越的社区结构,并确定了一个含有70%欺诈账户的高风险社区.

结论:

  • 量子计算通过网络社区分析为欺诈检测提供了更快,更有效的方法.
  • 拟议的方法对金融机构在其反欺诈战略中具有实际实用性.
  • 这项研究强调了量子计算在大数据安全应用中的潜力.