This paper presents a new Quantum Machine Learning model where classical data gets processed on a quantum computer. We propose a hybrid Quantum-Classical model composed of three elements: a classical computer in charge of the data preparation and interpretation; a Gate-based Quantum Computer running the VQA (Variational Quantum Algorithm) representing the Quantum Neural Network; 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 Quantum Neural Networks 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. However, 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 and shows the feasibility of integration for gate-based and adiabatic quantum computing models, avoiding the barren plateau effect and opening the door to modern hybrid quantum machine learning approaches for High Performance Computing.