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Sampling Continuous Time Signal

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

Sampling Methods: Overview

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...

Sampling Methods: Sample Types

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 Theorem

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 Distribution

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...

Cluster Sampling Method

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...

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Related Experiment Video

Updated: May 15, 2026

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

Gaussian boson sampling with 1,024 squeezed states in 8,176 modes.

Hua-Liang Liu^1,2,3, Hao Su^1,2,3, Yu-Hao Deng^1,2,3

¹Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei, China.

|May 13, 2026

Summary

This summary is machine-generated.

This study introduces Jiuzhang 4.0, a programmable photonic quantum processor. It significantly scales up Gaussian boson sampling, overcoming photon loss challenges for fault-tolerant quantum computing.

More Related Videos

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

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Last Updated: May 15, 2026

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

Area of Science:

Quantum Computing
Photonic Systems
Quantum Information Science

Background:

Large-scale quantum processors are crucial for advancing computation beyond classical limits.
Gaussian boson sampling demonstrates quantum advantage and generates error-correcting codes.
Photon loss in complex circuits limits the scalability of photonic quantum systems.

Purpose of the Study:

To develop a programmable photonic quantum processor that overcomes scalability limitations.
To demonstrate a significant increase in the scale of Gaussian boson sampling.
To advance towards fault-tolerant quantum computing architectures.

Main Methods:

Utilized a programmable photonic quantum processor, Jiuzhang 4.0.
Incorporated 1,024 high-efficiency squeezed states into an 8,176-mode hybrid spatial-temporal encoded circuit.
Achieved high source (92%) and system (51%) efficiencies.

Main Results:

Generated samples with up to 3,050 detected photons, an order of magnitude increase.
Enabled sampling within a Hilbert space of dimension ~10^2,461.
Demonstrated rigorous validation against advanced classical simulation methods, including matrix product state algorithms.

Conclusions:

The developed processor pushes experimental frontiers beyond classical tractability.
Programmable low-loss quantum processors are key to realizing large-scale quantum systems.
This work paves the way for trillion-qumode cluster states and fault-tolerant photonic quantum hardware.