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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

3.7K
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Sampling Plans01:23

Sampling Plans

1.1K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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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...
15.3K
Sampling Distribution01:12

Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
18.7K
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

3.5K
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
3.5K
Random Sampling Method01:09

Random Sampling Method

15.5K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...
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Related Experiment Video

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Quantum Ensemble Classification: A Sampling-Based Learning Control Approach.

Chunlin Chen, Daoyi Dong, Bo Qi

    IEEE Transactions on Neural Networks and Learning Systems
    |January 24, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel supervised quantum learning approach for quantum ensemble classification (QEC). The sampling-based learning control method effectively discriminates between quantum states, even for complex inhomogeneous ensembles.

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    Area of Science:

    • Quantum physics
    • Quantum information science
    • Machine learning

    Background:

    • Quantum ensemble classification (QEC) is crucial for applications like atomic discrimination and isotope separation.
    • Distinguishing nonorthogonal quantum states deterministically is impossible due to quantum mechanics.
    • Classifying inhomogeneous quantum ensembles presents challenges due to internal parameter variations.

    Purpose of the Study:

    • To develop a systematic methodology for quantum ensemble classification (QEC) by framing it as a supervised quantum learning problem.
    • To present a novel discrimination method for similar quantum systems.
    • To introduce a sampling-based learning control (SLC) approach for QEC.

    Main Methods:

    • Recasting QEC as a supervised quantum learning task.
    • Employing a sampling-based learning control (SLC) approach for quantum discrimination.
    • Steering quantum ensemble members of different classes towards distinct target states, such as orthogonal states.

    Main Results:

    • A new discrimination method is proposed for differentiating two similar quantum systems.
    • The SLC method is presented and validated for QEC.
    • Numerical simulations confirm the approach's effectiveness for binary and multiclass classification of quantum ensembles.

    Conclusions:

    • The proposed supervised quantum learning methodology, utilizing SLC, offers an effective solution for quantum ensemble classification.
    • The approach successfully addresses the challenges posed by inhomogeneous quantum ensembles and nonorthogonal states.
    • Demonstrated effectiveness in both binary and multiclass classification scenarios highlights the method's versatility.