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

Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

20.7K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
20.7K
Quadratic Models01:23

Quadratic Models

344
Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
344
Randomized Experiments01:13

Randomized Experiments

9.3K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.3K
Synthetic Disvision of Polynomials01:28

Synthetic Disvision of Polynomials

344
Synthetic division is an efficient algorithmic approach for dividing a polynomial by a linear binomial of the form x - c, where c is a real number. This method is helpful due to its streamlined process, which avoids the more cumbersome steps involved in the traditional long division of polynomials. It simplifies computation and serves as a practical tool for evaluating polynomials and identifying their factors.To perform synthetic division, one begins by listing the coefficients of the...
344
The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

62.0K
Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing...
62.0K
Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

664
Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the...
664

You might also read

Related Articles

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

Sort by
Same author

Self-Powered Smart Textiles for Accelerated Wound Healing through Band Alignment in Piezoelectric Heterojunctions.

ACS nano·2026
Same author

Methylation-regulated miR-374a-5p and miR-374b-5p suppress glycolysis and malignant progression of head and neck squamous cell carcinoma by targeting DEPDC1.

Frontiers in oncology·2026
Same author

A pH-responsive layered double hydroxide nanoradiosensitizer for bone metastasis tumor.

Materials today. Bio·2026
Same author

Integrated radiopathomics nomogram for predicting angiogenic microvascular patterns in NSCLC: a dual-center validation study.

Annals of medicine·2026
Same author

High-Accuracy Temporal Prediction via Experimental Quantum Reservoir Computing in Correlated Spins.

Physical review letters·2026
Same author

Nanoscale Icelike Water Layer on a Diamond Surface under Ambient Conditions.

Physical review letters·2026
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
See all related articles

Related Experiment Video

Updated: Apr 14, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.8K

Experimental realization of a quantum support vector machine.

Zhaokai Li1,2, Xiaomei Liu1, Nanyang Xu1,2

  • 1Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China.

Physical Review Letters
|April 25, 2015
PubMed
Summary
This summary is machine-generated.

Quantum machine learning offers faster AI by learning from data. This study demonstrates a quantum algorithm for handwriting recognition, showing potential speedups for big data challenges.

More Related Videos

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.8K
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

1.2K

Related Experiment Videos

Last Updated: Apr 14, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.8K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.8K
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

1.2K

Area of Science:

  • Artificial Intelligence
  • Quantum Computing
  • Machine Learning

Background:

  • Classical machine learning demands significant computational resources, especially with big data.
  • Quantum machine learning algorithms promise exponential speedups over classical methods through quantum parallelism.

Purpose of the Study:

  • To demonstrate a quantum machine learning algorithm for handwriting recognition.
  • To explore the potential of quantum computing in addressing big data challenges in AI.

Main Methods:

  • Implementation of a quantum machine learning algorithm on a four-qubit Nuclear Magnetic Resonance (NMR) test bench.
  • Training the quantum machine on standard character fonts for recognition tasks.

Main Results:

  • Successful demonstration of handwriting recognition using a quantum machine learning algorithm.
  • The quantum machine learned standard fonts and recognized handwritten characters from a two-candidate set.

Conclusions:

  • Quantum machine learning algorithms show promise for accelerating artificial intelligence tasks.
  • Quantum speedup is highly attractive for handling big data challenges due to the computational demands of classical AI.