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

NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

672
When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
672
Improving Translational Accuracy02:07

Improving Translational Accuracy

9.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
9.1K
Phasor Arithmetics01:13

Phasor Arithmetics

243
Phasors and their corresponding sinusoids are interrelated, offering unique insights into the behavior of alternating current (AC) circuits. One way to understand this relationship is through the operations of differentiation and integration in both the time and phasor domains.
When the derivative of a sinusoid is taken in the time domain, it transforms into its corresponding phasor multiplied by j-omega (jω) in the phasor domain, where j is the imaginary unit, and ω is the angular...
243
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

545
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...
545
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.0K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.0K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

64
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
64

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Accelerating scientific discovery with Co-Scientist.

Nature·2026
Same author

Advancing conversational diagnostic AI with multimodal reasoning.

Nature medicine·2026
Same author

Cardiovascular Disease in a Population-Based Cohort of Prostate Cancer Patients With Long-Term Follow-Up.

Clinical genitourinary cancer·2026
Same author

Investments in photoreceptors compete with investments in optics to determine eye design.

eLife·2026
Same author

Efficient Near-Optimal Decoding of the Surface Code through Ensembling.

Physical review letters·2026
Same author

Advancing regulatory variant effect prediction with AlphaGenome.

Nature·2026
Same journal

Family of magnetic field-boosted superconductors in rhombohedral graphene.

Nature·2026
Same journal

What's the human cost of US research turmoil? A new film finds out.

Nature·2026
Same journal

Daily briefing: Ovaries start a second job after menopause.

Nature·2026
Same journal

Audio long read: Is the peptide craze backed by science? The promise behind the hype.

Nature·2026
Same journal

Scientists fight back against far-right plans to restrict academic freedom in Germany.

Nature·2026
Same journal

How AI can crack open the 'hidden curriculum' for neurodivergent students.

Nature·2026
Ver todos los artículos relacionados

Video Experimental Relacionado

Updated: Jun 7, 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

487

Aprender decodificación de errores de alta precisión para procesadores cuánticos

Johannes Bausch1, Andrew W Senior2, Francisco J H Heras3

  • 1Google DeepMind, London, UK. jbausch@google.com.

Nature
|November 20, 2024
PubMed
Resumen
Este resumen es generado por máquina.

Un nuevo decodificador de red neuronal mejora significativamente la corrección de errores cuánticos al interpretar con precisión los datos ruidosos de las computadoras cuánticas. Este enfoque de aprendizaje automático mejora la confiabilidad de los cálculos cuánticos y ayuda a construir sistemas cuánticos a gran escala.

Más Videos Relacionados

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

14.4K
Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

12.8K

Videos de Experimentos Relacionados

Last Updated: Jun 7, 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

487
Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

14.4K
Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

12.8K

Área de la Ciencia:

  • La computación cuántica
  • Corrección del error cuántico
  • Aprendizaje automático

Sus antecedentes:

  • La corrección de errores cuánticos es crucial para construir computadoras cuánticas a gran escala.
  • Los códigos cuánticos de corrección de errores codifican información redundantemente a través de múltiples qubits.
  • La decodificación precisa de la información del síndrome ruidoso es un desafío clave.

Objetivo del estudio:

  • Desarrollar un decodificador basado en el aprendizaje automático para el código de superficie, un código líder de corrección de errores cuánticos.
  • Para mejorar la precisión de la decodificación de la información del síndrome ruidoso para la computación cuántica.

Principales métodos:

  • Desarrolló una red neuronal recurrente basada en transformadores.
  • Entrenó la red con datos simulados y del mundo real del procesador cuántico Sycamore de Google.
  • Utilizó lecturas suaves e información de fugas para mejorar la decodificación.

Principales resultados:

  • El decodificador de la red neuronal superó a los decodificadores de última generación en los datos del mundo real para los códigos de superficie de distancia-3 y distancia-5.
  • Se mantiene la ventaja de rendimiento en datos simulados hasta la distancia 11 con ruido realista.
  • Adaptación demostrada a distribuciones de error desconocidas utilizando muestras experimentales.

Conclusiones:

  • El aprendizaje automático puede superar a los algoritmos diseñados por humanos en la decodificación de errores cuánticos.
  • El decodificador desarrollado muestra un gran potencial para su aplicación práctica en ordenadores cuánticos.
  • Este trabajo destaca el poder de los enfoques basados en datos en el avance de las tecnologías cuánticas.