Training Neural Networks with Universal Adiabatic Quantum Computing
Steve Abel Juan Carlos Criado Michael Spannowsky
The training of neural networks (NNs) is a computationally intensive task
requiring significant time and resources. This paper presents a novel approach
to NN training using Adiabatic Quantum Computing (AQC), a paradigm that
leverages the principles of adiabatic evolution to solve optimisation problems.
We propose a universal AQC method that can be implemented on gate quantum
computers, allowing for a broad range of Hamiltonians and thus enabling the
training of expressive neural networks. We apply this approach to various
neural networks with continuous, discrete, and binary weights. Our results
indicate that AQC can very efficiently find the global minimum of the loss
function, offering a promising alternative to classical training methods.