Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

545
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...
545
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

41
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
41
Linear time-invariant Systems01:23

Linear time-invariant Systems

216
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
216
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

64
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
64
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

85
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
85
Machines: Problem Solving II01:30

Machines: Problem Solving II

296
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
296

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Deep Learning-Derived Retinal Age Detects Cognitive Impairment.

Ophthalmology science·2026
Same author

ASO-RASAR: A Read-Across Framework for Predicting Antisense Oligonucleotide Gapmer Activity Across Target Genes.

Journal of chemical information and modeling·2026
Same author

A review of animal-assisted therapy for older adults in Korea: effects on depression and cognitive function and implications for practice.

Journal of animal science and technology·2026
Same author

High-performance thermally-robust C-band GeSi FK electro-absorption modulators on 300-mm silicon photonics platform.

Optics express·2026
Same author

Development of an Artificial Intelligence Model to Predict Endotracheal Intubation in Critically Ill Patients in Real Time.

Journal of clinical medicine·2026
Same author

CO<sub>2</sub> Hydrogenation to Methanol on Core-Shell-Structured SiO<sub>2</sub>-Encapsulated Cu-ZnO-In<sub>2</sub>O<sub>3</sub> Nanoparticles.

ChemSusChem·2026

Related Experiment Video

Updated: Jun 6, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

487

Assessing the feasibility of quantum learning algorithms for noisy linear problems.

Minkyu Kim1, Panjin Kim2

  • 1The Affiliated Institute of ETRI, Daejeon, 34044, Korea.

Scientific Reports
|November 25, 2024
PubMed
Summary

This study enhances quantum algorithms for noisy linear problems, extending quantum Fourier transform applicability. It also reveals efficient classical algorithms for specific learning with errors problems using quantum samples.

Keywords:
Bernstein-Vazirani algorithmLearning with errorsMachine learningQuantum Fourier TransformQuantum algorithm

More Related Videos

Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

12.8K
Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

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

Published on: May 30, 2014

14.4K

Related Experiment Videos

Last Updated: Jun 6, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

487
Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

12.8K
Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

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

Published on: May 30, 2014

14.4K

Area of Science:

  • Quantum computing
  • Cryptography
  • Computational complexity

Background:

  • Existing quantum algorithms for noisy linear problems rely on specific assumptions.
  • The ring learning with errors problem is a key challenge in this area.
  • Previous work established polynomial-time quantum algorithms for noisy linear problems with quantum samples.

Purpose of the Study:

  • To reexamine quantum algorithms for noisy linear problems.
  • To extend the applicability of the quantum Fourier transform to the ring learning with errors problem.
  • To investigate the existence of efficient classical algorithms for related problems.

Main Methods:

  • Reexamination of existing quantum algorithms under standard assumptions.
  • Application of quantum Fourier transform techniques.
  • Analysis of classical algorithms for specific lattice-based problems.

Main Results:

  • Extended applicability of the quantum Fourier transform to the ring learning with errors problem.
  • Demonstrated efficient classical algorithms for short integer solution and size-reduced learning with errors problems when quantum samples are provided.

Conclusions:

  • The findings broaden the scope of quantum algorithms for noisy linear problems.
  • Efficient classical solutions are possible for certain learning with errors variants under specific conditions.
  • This work bridges quantum and classical approaches to solving complex computational problems.