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The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

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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.
41.9K
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Quantitative Analysis01:12

Quantitative Analysis

247
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...
247
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
4.2K
Electron Orbital Model01:18

Electron Orbital Model

67.5K
Orbitals are the areas outside of the atomic nucleus where electrons are most likely to reside. They are characterized by different energy levels, shapes, and three-dimensional orientations. The location of electrons is described most generally by a shell or principal energy level, then by a subshell within each shell, and finally, by individual orbitals found within the subshells.
The first shell is closest to the nucleus, and it has only one subshell with a single spherical orbital called the...
67.5K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

77
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
77

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相关实验视频

Updated: Jun 6, 2025

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

一种混合量子-经典模型用于股票价格预测,使用量子增强的长短期记忆.

Kimleang Kea1, Dongmin Kim1, Chansreynich Huot1

  • 1Department of AI Convergence, Pukyong National University, Nam-gu, Busan 48513, Republic of Korea.

Entropy (Basel, Switzerland)
|November 27, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了QLSTM,这是用于股票价格预测的混合量子-经典机器学习模型. QLSTM显著优于经典模型,在金融市场预测中显示出更高的准确性和更少的错误.

关键词:
在金融领域的AI.长期短期记忆 长期短期记忆量子机器学习就是量子机器学习.股票价格预测 股票价格预测时间序列分析分析时间序列分析

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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

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相关实验视频

Last Updated: Jun 6, 2025

Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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

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科学领域:

  • 量子计算是一种量子计算.
  • 机器学习 机器学习
  • 金融市场 金融市场

背景情况:

  • 股票市场预测是机器学习 (ML) 中的一个复杂挑战.
  • 经典的ML模型用于预测是计算密集的.
  • 量子计算 (QC) 提供了比经典计算机加速指数的潜力.

研究的目的:

  • 开发和评估一种混合量子-经典的ML模型,用于股票价格预测.
  • 通过将经典的长短期记忆 (LSTM) 与QC集成,引入一种新型模型,即量子长短期记忆 (QLSTM).
  • 将QLSTM的性能与经典的ML模型进行比较.

主要方法:

  • 开发了一个混合量子-经典ML模型 (QLSTM).
  • 使用IBM量子模拟器和一个真实的IBM量子计算机验证了QLSTM.
  • 使用根平均平方误差 (RMSE) 和预测准确度评估性能.
  • 对经典模型进行了比较分析,并探索了超参数的影响.

主要成果:

  • 与经典LSTM (0.0693) 相比,QLSTM获得了较低的RMSE (0.0602).
  • QLSTM的预测准确度比经典的LSTM (0.8815) 高 (0.9736).
  • 在RMSE和精度指标方面,QLSTM的表现优于其他经典模型.

结论:

  • 混合QLSTM模型在股票价格预测方面表现出卓越的性能.
  • 将QC与经典的ML集成在财务预测中提供了显著的优势.
  • 在金融市场应用量子计算方面,QLSTM是一个有前途的进步.