Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
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...
100
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

183
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
183
Heuristics01:21

Heuristics

148
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
148
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.7K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.7K
Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

250
Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
250
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

124
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
124

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Qutrit toric code and parafermions in trapped ions.

Nature communications·2025
Same author

Non-Abelian topological order and anyons on a trapped-ion processor.

Nature·2024
Same author

Volume-Law to Area-Law Entanglement Transition in a Nonunitary Periodic Gaussian Circuit.

Physical review letters·2023
Same author

Bogoliubov-Born-Green-Kirkwood-Yvon Hierarchy and Generalized Hydrodynamics.

Physical review letters·2022
Same author

Duality between Weak and Strong Interactions in Quantum Gases.

Physical review letters·2022

Video Experimental Relacionado

Updated: Sep 9, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.3K

Evaluación comparativa de un algoritmo heurístico adiabático de Floquet para el problema de Max-Cut

Etienne Granet1, Henrik Dreyer2

  • 1Quantinuum, Leopoldstrasse 180, 80804, Munich, Germany. etienne.granet@quantinuum.com.

Scientific reports
|August 30, 2025
PubMed
Resumen
Este resumen es generado por máquina.

La evolución adiabática de Floquet ofrece un enfoque de computación cuántica más eficiente para problemas de optimización. Este método reduce significativamente el recuento de puertas, lo que permite soluciones óptimas para problemas como Max-Cut en computadoras cuánticas.

Más Videos Relacionados

Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.2K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.7K

Videos de Experimentos Relacionados

Last Updated: Sep 9, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.3K
Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.2K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.7K

Área de la Ciencia:

  • Mecánica Cuántica
  • Ciencias computacionales
  • Algoritmos de optimización

Sus antecedentes:

  • El teorema adiabático de la mecánica cuántica establece que un sistema en su estado fundamental permanece en el estado fundamental bajo cambios hamiltonianos lentos.
  • Los principios de computación cuántica adiabática pueden resolver problemas complejos, pero a menudo requieren grandes conteos de puertas en computadoras cuánticas digitales debido al escalamiento de pasos de Trotter.

Objetivo del estudio:

  • Para investigar un nuevo enfoque, la evolución adiabática de Floquet, para implementar de manera eficiente la dinámica adiabática en computadoras cuánticas digitales.
  • Demostrar la efectividad de la evolución adiabática de Floquet para resolver problemas de optimización clásicos, específicamente el problema de Max-Cut.

Principales métodos:

  • Evolución adiabática de Floquet propuesta, utilizando un paso de Trotter fijo y finito para la dinámica adiabática.
  • Se utilizaron simulaciones de estado de la matriz del producto para proporcionar pruebas numéricas de la eficacia del método.
  • Probado el enfoque en el problema de Max-Cut para 3 gráficos regulares.

Principales resultados:

  • La evolución adiabática de Floquet reduce significativamente el recuento de puertas en varios órdenes de magnitud en comparación con la evolución adiabática en tiempo continuo.
  • Las simulaciones numéricas muestran soluciones óptimas para el problema Max-Cut en gráficos 3-regulares con bajos tiempos de ejecución y dimensiones de enlace.
  • La estimación de recursos sugiere el potencial de las computadoras cuánticas para superar a los solucionadores clásicos para este problema.

Conclusiones:

  • La evolución adiabática de Floquet presenta una alternativa computacionalmente eficiente para la computación cuántica adiabática.
  • Este método es prometedor para resolver problemas de optimización difíciles como Max-Cut en dispositivos cuánticos a corto plazo.