Adiabatic training for Variational Quantum Algorithms

TL;DR

This paper introduces a hybrid quantum machine learning model that integrates adiabatic quantum optimization with variational quantum algorithms, offering a new approach to train quantum neural networks beyond traditional gradient-based methods.

Contribution

It is the first to explore using adiabatic quantum computers for training variational quantum algorithms, demonstrating potential advantages over classical gradient-based optimizers.

Findings

Adiabatic optimization shows promising results compared to classical methods.

Hybrid quantum models are feasible and can be integrated effectively.

Potential to overcome barren-plateau issues in quantum neural network training.

Abstract

This paper presents a new hybrid Quantum Machine Learning (QML) model composed of three elements: a classical computer in charge of the data preparation and interpretation; a Gate-based Quantum Computer running the Variational Quantum Algorithm (VQA) representing the Quantum Neural Network (QNN); and an adiabatic Quantum Computer where the optimization function is executed to find the best parameters for the VQA. As of the moment of this writing, the majority of QNNs are being trained using gradient-based classical optimizers having to deal with the barren-plateau effect. Some gradient-free classical approaches such as Evolutionary Algorithms have also been proposed to overcome this effect. To the knowledge of the authors, adiabatic quantum models have not been used to train VQAs. The paper compares the results of gradient-based classical algorithms against adiabatic optimizers showing the feasibility of integration for gate-based and adiabatic quantum computing models, opening the door to modern hybrid QML approaches for High Performance Computing.