Quantum Graph Neural Networks for Portfolio Optimization in Complex Financial Markets, A Novel Approach

Quantum machine learning is an emerging field, combining ideas from quantum computing and machine learning. In this paper we propose a novel quantum graph neural network for modelling financial time series. We demonstrate a proof of concept model implemented in pytorch-geometric and pennylane and benchmark the results on the Cora dataset. Further, we apply the model to financial time series data(NIFTY50) where we predict the movement of stock prices.