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

The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

60.1K
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 hydrogen spectra.
60.1K
Quantum Numbers02:43

Quantum Numbers

52.8K
It is said that the energy of an electron in an atom is quantized; that is, it can be equal only to certain specific values and can jump from one energy level to another but not transition smoothly or stay between these levels.
52.8K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

1.2K
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 the...
1.2K
Quantitative Analysis01:12

Quantitative Analysis

1.6K
Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
1.6K
Machines: Problem Solving II01:30

Machines: Problem Solving II

700
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.
700
Machines01:19

Machines

594
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
594

You might also read

Related Articles

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

Sort by
Same author

Retrocausal Capacity of a Quantum Channel: Communicating through Noisy Closed Timelike Curves.

Physical review letters·2026
Same author

QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation.

NPJ quantum information·2026
Same author

Quantum Stroboscopy for Time Measurements.

Physical review letters·2026
Same author

Hybrid Quantum-Classical Algorithm for Robust Optimization via Stochastic-Gradient Online Learning.

Quantum machine intelligence·2026
Same author

Effects of anesthesia modality on plasma proteomics and biomarkers of inflammation and vascular injury: an exploratory analysis.

Canadian journal of anaesthesia = Journal canadien d'anesthesie·2025
Same author

Hardware-efficient quantum error correction via concatenated bosonic qubits.

Nature·2025
Same journal

Retraction Note: NSD2 targeting reverses plasticity and drug resistance in prostate cancer.

Nature·2026
Same journal

Enhanced B cell priming induces broadly neutralizing HIV-1 apex antibodies.

Nature·2026
Same journal

Vaccination elicits HIV broadly neutralizing antibodies in primates.

Nature·2026
Same journal

Child online safety needs more than social-media bans.

Nature·2026
Same journal

Ebola preparedness must start with ecosystems and before humans show symptoms.

Nature·2026
Same journal

AI tools can speed up thinking, but evidence still comes from the lab bench.

Nature·2026
See all related articles

Related Experiment Video

Updated: Feb 22, 2026

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

Quantum machine learning.

Jacob Biamonte1,2, Peter Wittek3, Nicola Pancotti4

  • 1Quantum Complexity Science Initiative, Skolkovo Institute of Science and Technology, Skoltech Building 3, Moscow 143026, Russia.

Nature
|September 15, 2017
PubMed
Summary
This summary is machine-generated.

Quantum computing may accelerate machine learning by leveraging unique quantum patterns. Researchers are developing quantum machine learning algorithms, but significant hardware and software hurdles remain for practical applications.

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

13.3K

Related Experiment Videos

Last Updated: Feb 22, 2026

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
Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

13.3K

Area of Science:

  • Computer Science
  • Quantum Physics
  • Artificial Intelligence

Background:

  • Machine learning (ML) utilizes computational power and algorithms to identify data patterns.
  • Quantum systems exhibit unique patterns potentially offering advantages over classical systems for ML tasks.

Purpose of the Study:

  • To explore the potential of quantum computing for enhancing machine learning.
  • To investigate the development of quantum machine learning (QML) algorithms and software.

Main Methods:

  • Review of current advancements in quantum algorithms relevant to ML.
  • Analysis of the theoretical underpinnings of quantum advantage in pattern recognition.

Main Results:

  • Quantum algorithms show promise as foundational components for ML programs.
  • Quantum systems are hypothesized to efficiently process patterns intractable for classical computers.

Conclusions:

  • Quantum machine learning is a promising field with the potential for significant speedups over classical ML.
  • Substantial hardware and software challenges must be overcome to realize practical quantum machine learning applications.