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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Training neural networks with universal adiabatic quantum computing.

Steve Abel1,2, Juan Carlos Criado3, Michael Spannowsky1

  • 1Institute for Particle Physics Phenomenology, Durham University, Durham, United Kingdom.

Frontiers in Artificial Intelligence
|July 8, 2024
PubMed
Summary
This summary is machine-generated.

Adiabatic quantum computing (AQC) offers an efficient method for training neural networks (NNs). This novel approach leverages quantum principles to find optimal solutions, presenting a promising alternative to traditional NN training techniques.

Keywords:
NN trainingadiabatic quantum computingbinary neural networksneural networksquantum computing

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Area of Science:

  • Quantum Computing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Neural network (NN) training is resource-intensive.
  • Classical training methods face computational challenges.

Purpose of the Study:

  • To introduce a novel adiabatic quantum computing (AQC) approach for NN training.
  • To demonstrate the universal applicability of AQC on gate quantum computers for diverse NN architectures.

Main Methods:

  • Developed a universal AQC method implementable on gate quantum computers.
  • Applied the AQC method to train neural networks with continuous, discrete, and binary weights.

Main Results:

  • Adiabatic quantum computing (AQC) efficiently finds the global minimum of the loss function.
  • The AQC approach enables the training of expressive neural networks.

Conclusions:

  • AQC presents a powerful and efficient alternative for training neural networks.
  • This quantum computing paradigm can significantly accelerate and optimize machine learning tasks.