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Thermal Energy Microscopically, thermal energy is the kinetic energy associated with the random motion of atoms and molecules. Temperature is a quantitative measure of “hot” or “cold”, which depends on the amount of thermal energy. When the atoms and molecules in an object are moving or vibrating quickly, they have a higher average kinetic energy (KE) (or higher thermal energy), and the object is perceived as “hot”, or it is described as being at a...
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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).
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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.
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The spontaneity of a process depends upon the temperature of the system. Phase transitions, for example, will proceed spontaneously in one direction or the other depending upon the temperature of the substance in question. Likewise, some chemical reactions can also exhibit temperature-dependent spontaneities. To illustrate this concept, the equation relating free energy change to the enthalpy and entropy changes for the process is considered:
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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?
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A comparative analysis of classical machine learning models with quantum-inspired models for predicting world surface

Trilok Nath Pandey1, Vishvajeet Ravalekar2, Sidharth D Nair3

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, 600127, India. triloknath.pandey@vit.ac.in.

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Summary
This summary is machine-generated.

This study compares classical and quantum machine learning for time-series analysis. Quantum machine learning shows potential for complex data, offering new avenues for accurate forecasting in various industries.

Keywords:
AutoregressiveAutoregressive moving averageHybrid quantum neural networkIntegrated moving averageLong-short term memoryNoisy intermediate scale quantumParameterized quantum circuitQuadratic unconstrained binary optimizationQuantum long-short term memoryQuantum neural networksQuantum regressorQuantum support vector classifierQuantum support vector regressorSeasonal autoregressive integrated moving averageVariationalVariational quantum circuitVariational quantum eigen-solver

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Area of Science:

  • Computational Science
  • Quantum Computing
  • Machine Learning

Background:

  • The increasing volume and complexity of time-series data necessitate advanced computational models for efficient analysis and prediction.
  • Traditional machine learning algorithms face challenges in handling subtle temporal patterns within large datasets.

Purpose of the Study:

  • To compare the performance and time complexity of classical machine learning algorithms against quantum machine learning algorithms for time-series data analysis.
  • To evaluate the benefits and drawbacks of quantum machine learning in the time-series domain.

Main Methods:

  • Utilized a global temperature records dataset spanning fifty years.
  • Empirically analyzed and compared classical machine learning algorithms with quantum algorithms leveraging superposition and entanglement.

Main Results:

  • Quantum machine learning algorithms demonstrated unique capabilities in handling subtle temporal patterns.
  • Rigorous empirical analysis provided insights into the comparative performance and time complexity.

Conclusions:

  • Quantum machine learning offers a promising approach for enhanced time-series analysis and prediction.
  • Findings support the applicability of quantum algorithms in real-world scenarios, impacting fields like finance and healthcare.