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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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Training an Ising machine with equilibrium propagation.

Jérémie Laydevant1, Danijela Marković2, Julie Grollier3

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This study introduces a supervised training method for Ising machines, enabling efficient training of neural networks for Artificial Intelligence (AI) applications. The approach achieves high accuracy comparable to software, paving the way for advanced AI hardware.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Physics

Background:

  • Ising machines, hardware implementations of the Ising model, have historical ties to early Artificial Intelligence (AI) algorithms.
  • Direct application of supervised learning methods to Ising machines has been a significant challenge, limiting their accuracy in AI tasks.
  • Existing limitations hinder the full potential of Ising machines in modern AI development.

Purpose of the Study:

  • To develop and demonstrate an efficient supervised training approach for Ising machines.
  • To bridge the gap between Ising machine physics and essential supervised training methods for AI.
  • To explore the potential of Ising machines as trainable hardware for advanced AI applications.

Main Methods:

  • Utilized the Equilibrium Propagation algorithm for supervised training of Ising machines.
  • Employed the D-Wave Ising machine's quantum annealing procedure for neural network training.
  • Applied the approach to train a fully-connected neural network on the MNIST dataset.

Main Results:

  • Achieved comparable accuracy to software-based implementations in supervised training.
  • Demonstrated that Ising machine connectivity supports convolution operations.
  • Successfully trained a compact convolutional network using minimal spins per neuron.

Conclusions:

  • Ising machines can be efficiently trained in a supervised manner using Equilibrium Propagation.
  • The D-Wave Ising machine is a viable platform for training neural networks, including convolutional networks.
  • This work establishes Ising machines as a promising trainable hardware for enhancing AI and machine learning applications.