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

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
Stability of Equilibrium Configuration: Problem Solving01:13

Stability of Equilibrium Configuration: Problem Solving

The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
Problem-solving in the context of the stability of equilibrium configuration...
Randomized Experiments01:13

Randomized Experiments

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...
Biot-Savart Law: Problem-Solving00:59

Biot-Savart Law: Problem-Solving

The magnitude and direction of a magnetic field created by a steady current can be calculated using the Biot-Savart law.
Consider a mobile phone battery bank as a source of steady current, which flows through the wire connected between the two. What is the magnitude of the magnetic field created by this current at a field point P?
To estimate the magnitude of the total magnetic field, we first consider a small current element of length dl, at a distance r from the field point. Now the following...
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

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 problem,...

You might also read

Related Articles

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

Sort by
Same author

Demonstrating real advantage of machine learning-enhanced Monte Carlo for combinatorial optimization.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Rare trajectories in a prototypical mean-field disordered model: Insights into landscape and instantons.

Physical review. E·2026
Same author

Functional bottlenecks can emerge from non-epistatic underlying traits.

PLoS computational biology·2026
Same author

Quenched properties of the spectral form factor.

Physical review. E·2026
Same author

Author Correction: Exploring the space of self-reproducing ribozymes using generative models.

Nature communications·2025
Same author

Performance of machine-learning-assisted Monte Carlo in sampling from simple statistical physics models.

Physical review. E·2025
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: Jun 5, 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

Solvable model of quantum random optimization problems.

Laura Foini1, Guilhem Semerjian, Francesco Zamponi

  • 1LPTENS, CNRS UMR 8549, associée à l'UPMC Paris 06, 24 Rue Lhomond, 75005 Paris, France.

Physical Review Letters
|January 15, 2011
PubMed
Summary
This summary is machine-generated.

We investigated a quantum model for optimization problems, revealing a complex energy spectrum with unique transitions. This quantum behavior may significantly impact quantum computing algorithms for problem-solving.

Related Experiment Videos

Last Updated: Jun 5, 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

Area of Science:

  • Quantum computing
  • Optimization problems
  • Condensed matter physics

Background:

  • Optimization problems are crucial in various scientific and industrial fields.
  • Quantum computing offers potential advantages for solving complex optimization tasks.
  • Understanding the quantum behavior of simplified models is key to developing quantum algorithms.

Purpose of the Study:

  • To analyze the quantum version of a simplified optimization problem model.
  • To investigate the effects of quantum fluctuations introduced by a transverse field.
  • To characterize the low-energy spectrum of the quantum Hamiltonian.

Main Methods:

  • Studying a simplified quantum model of optimization problems.
  • Introducing quantum fluctuations via a transverse field acting on qubits.
  • Analyzing the quantum Hamiltonian's low-energy spectrum.

Main Results:

  • A complex low-energy spectrum was found for the quantum Hamiltonian.
  • An abrupt condensation transition was observed.
  • A continuum of level crossings was identified as a function of the transverse field.

Conclusions:

  • The complex spectral structure has significant implications for quantum algorithms.
  • This study provides insights into the behavior of quantum systems for optimization.
  • Further research is needed to explore the practical applications in quantum computing.