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Related Concept Videos

Sampling Theorem01:15

Sampling Theorem

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.
Sampling Methods: Overview01:06

Sampling Methods: Overview

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 sampling...
Bandpass Sampling01:17

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
Cluster Sampling Method01:20

Cluster Sampling Method

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...
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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...
Sampling Distribution01:12

Sampling Distribution

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: May 15, 2026

Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source
12:19

Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

Published on: April 4, 2017

Boson sampling on a photonic chip.

Justin B Spring1, Benjamin J Metcalf, Peter C Humphreys

  • 1Clarendon Laboratory, Department of Physics, University of Oxford, Oxford, UK. j.spring1@physics.ox.ac.uk

Science (New York, N.Y.)
|December 22, 2012
PubMed
Summary
This summary is machine-generated.

Researchers built a quantum boson-sampling machine (QBSM) using photon interference. This device demonstrates a potential quantum speedup for specific computational problems, paving the way for future quantum-enhanced computation.

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Generation and Coherent Control of Pulsed Quantum Frequency Combs
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Generation and Coherent Control of Pulsed Quantum Frequency Combs

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Related Experiment Videos

Last Updated: May 15, 2026

Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source
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Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source

Published on: April 4, 2017

High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip
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Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Area of Science:

  • Quantum Information Science
  • Photonic Quantum Computing
  • Computational Complexity

Background:

  • Universal quantum computers face significant construction challenges.
  • Problem-specific quantum algorithms offer a potential quantum speedup.
  • Boson sampling is a promising candidate for early quantum advantage.

Purpose of the Study:

  • To construct and benchmark a quantum boson-sampling machine (QBSM).
  • To demonstrate sampling from a distribution intractable for classical computers.
  • To analyze sources of error in photonic quantum sampling.

Main Methods:

  • Utilized an integrated photonic circuit for nonclassical photon interference.
  • Employed indistinguishable photons, linear optical elements, and single-photon detectors.
  • Benchmarked the QBSM with three and four photons.

Main Results:

  • Successfully sampled output distributions from the QBSM.
  • Identified and analyzed sources of sampling inaccuracy.
  • Demonstrated the feasibility of boson sampling with current technology.

Conclusions:

  • The developed QBSM represents a step towards practical quantum-enhanced computation.
  • Boson sampling is achievable with simpler requirements than universal quantum computation.
  • Scaling up QBSM technology could provide the first definitive quantum advantage.