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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

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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 Methods: Sample Types01:18

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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...
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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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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Sampling Theorem01:15

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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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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...
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Related Experiment Video

Updated: Aug 7, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Quantum deep learning by sampling neural nets with a quantum annealer.

Catherine F Higham1, Adrian Bedford2

  • 1School of Computing Science, University of Glasgow, Glasgow, G12 8QQ, UK. Catherine.Higham@glasgow.ac.uk.

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We show how to run deep neural networks on quantum annealers for faster image classification. This quantum machine learning approach could speed up AI tasks by over ten times.

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Area of Science:

  • Quantum computing
  • Artificial intelligence
  • Machine learning

Background:

  • Deep neural networks (DNNs) are powerful AI tools.
  • Quantum annealers offer fast sampling capabilities.

Purpose of the Study:

  • To frame a DNN as an energy-based model for quantum annealers.
  • To address challenges in high-resolution image classification on quantum processing units (QPUs).

Main Methods:

  • Transferring a pretrained convolutional neural network (CNN) to a QPU.
  • Developing methods to manage model states for quantum processing.

Main Results:

  • Successfully mapped a DNN onto a quantum processing unit.
  • Demonstrated feasibility of using quantum annealing for DNNs.
  • Identified potential for significant classification speedup.

Conclusions:

  • Quantum annealing can accelerate DNNs for image classification.
  • This hybrid quantum-classical approach shows promise for AI.
  • Further research can optimize quantum machine learning models.