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Area of Science:

  • * Condensed matter physics
  • * Quantum computing
  • * Artificial intelligence

Background:

  • * Artificial neural networks (ANNs) are computational models inspired by biological neural networks.
  • * Josephson junctions are key components in superconducting electronics, enabling unique quantum phenomena.
  • * Adiabatic quantum computing utilizes quantum fluctuations to find solutions to complex problems.

Purpose of the Study:

  • * To design simple analog artificial neural networks using adiabatic Josephson cells.
  • * To develop a gradient descent method for optimizing circuit parameters.
  • * To demonstrate the network's functionality with XOR and OR logical operations.

Main Methods:

  • * Design of analog ANNs utilizing adiabatic Josephson cells with a sigmoid activation function.
  • * Application of a gradient descent method for parameter adjustment.
  • * Implementation and testing of XOR and OR logic gate functionalities.

Main Results:

  • * Successful design of analog ANNs based on adiabatic Josephson cells.
  • * Efficient signal transmission between network layers achieved through parameter optimization.
  • * Demonstrated functionality for implementing XOR and OR logical operations.

Conclusions:

  • * Adiabatic Josephson cells offer a viable platform for analog ANNs.
  • * Gradient descent provides an effective method for parameter tuning in these networks.
  • * The proposed design is suitable for implementing basic logic functions.