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

42.4K
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.
42.4K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

72
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...
72
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

662
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...
662
Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

32.4K
sp3d and sp3d 2 Hybridization
32.4K
Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

47.2K
The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
47.2K
Modeling and Similitude01:12

Modeling and Similitude

284
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
284

You might also read

Related Articles

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

Sort by
Same author

A microscopic traffic characterization considering the impact of density on carbon emissions from CAVs.

Scientific reports·2026
Same author

SAluMC: Thwarting Side-Channel Attacks via Random Number Injection in RISC-V.

Entropy (Basel, Switzerland)·2025
Same author

QF-LCA dataset: Quantum Field Lens Coding Algorithm for system state simulation and strong predictions.

Data in brief·2024
Same author

Edge Computing for Effective and Efficient Traffic Characterization.

Sensors (Basel, Switzerland)·2023
Same author

Physical Layer Security in Two-Way SWIPT Relay Networks with Imperfect CSI and a Friendly Jammer.

Entropy (Basel, Switzerland)·2023
Same author

Guest Editorial: Special Issue on Artificial Intelligence in E-Healthcare and M-Healthcare.

Journal of healthcare engineering·2022

Related Experiment Video

Updated: Jul 15, 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

591

Quantum AI and hybrid simulators for a Universal Quantum Field Computation Model.

Philip Baback Alipour1, Thomas Aaron Gulliver1

  • 1Department of Electrical and Computer Engineering, University of Victoria, Victoria BC, V8W 2Y2, Canada.

Methodsx
|September 28, 2023
PubMed
Summary

Quantum field theory simulators use quantum AI to classify and predict system states. This novel approach enables strong event predictions across all scales, enhancing decision support systems.

Keywords:
Quantum Fourier transform (QFT)Quantum artificial intelligence (QAI)Quantum field computation model (QFCM)Quantum field theory (QFTh)Quantum simulatorQubitSuccess probabilityTransition probabilityUniversal QFCMUniversal Quantum Field Computation Model (UQFCM)

More Related Videos

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.8K
Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
05:39

Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

Published on: August 2, 2019

9.7K

Related Experiment Videos

Last Updated: Jul 15, 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

591
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.8K
Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform
05:39

Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

Published on: August 2, 2019

9.7K

Area of Science:

  • Quantum Field Theory (QFT)
  • Quantum Computing
  • Artificial Intelligence

Background:

  • Quantum field theory simulators utilize quantum circuits for simulating physical systems.
  • Quantum information (qubits) are processed via single field (SF) and quantum double field (QDF) transformations.
  • Existing models classify states based on pairwise particle states and state transition probabilities.

Purpose of the Study:

  • To present models classifying states against pairwise particle states using state transition probabilities.
  • To introduce a quantum AI (QAI) program for weighing and comparing field distances between entangled qubit states.
  • To develop a quantum-classical hybrid model for state classification and prediction by decoding qubits into classical bits.

Main Methods:

  • Utilizing quantum field computation models (QFCMs), such as QDF in IBM-QE, for state prediction.
  • Comparing various QFCMs to achieve a universal QFCM (UQFCM).
  • Employing QAI for simulating systems at any scale, achieving high measurement fidelity for state classification.

Main Results:

  • The UQFCM model demonstrates strong event prediction capabilities across all scales.
  • Selected optimal QFCMs achieve high fidelity in classifying states for QFTh observables.
  • The model predicts error rates in measurements, providing QAI output for user decision-making on efficient simulations.

Conclusions:

  • The UQFCM is a novel approach for strong event predictions in systems of any scale.
  • This model enhances decision support systems in various scientific and industrial applications.
  • QFCMs, when combined with QAI, offer reliable solutions for efficient system simulations and intelligent decision-making.