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This study introduces a thermodynamic computer using thermal fluctuations for nonlinear calculations. These thermodynamic neural networks can approximate functions, operating even out of equilibrium.

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Area of Science:

  • Physics
  • Computer Science
  • Thermodynamics

Background:

  • Classical neural networks perform complex computations but require significant energy.
  • Thermodynamic computing offers a potential low-energy alternative, traditionally focused on equilibrium systems.

Purpose of the Study:

  • To design a thermodynamic computer capable of arbitrary nonlinear calculations.
  • To explore the potential of thermodynamic systems operating out of equilibrium for computation.
  • To develop thermodynamic neural networks as universal function approximators.

Main Methods:

  • Designing simple thermodynamic circuits with fluctuating degrees of freedom.
  • Confining these circuits within a quartic potential and coupling them to a thermal bath.
  • Simulating a digital model of a thermodynamic neural network.
  • Utilizing a genetic algorithm to adjust network parameters.

Main Results:

  • Thermodynamic circuits exhibit activity as a nonlinear function of input, acting as thermodynamic neurons.
  • Networked circuits form thermodynamic neural networks capable of universal function approximation.
  • Simulations demonstrate successful nonlinear computation at specified times, irrespective of equilibrium status.

Conclusions:

  • Thermodynamic computing can be extended beyond equilibrium conditions.
  • This approach enables fully nonlinear computations analogous to classical neural networks.
  • The developed thermodynamic neural networks are powered by thermal fluctuations.