Variational quantum recurrent neural networks for multi-feature forecasting

Recurrent neural networks are a kind of recursion which takes sequence data as input and carries on the evolution of temporal dependency data. Variational quantum algorithms use classical computers as the quantum optimizer to update the circuit parameters. In this work, we propose a variational quantum algorithm of the recurrent neural network, which we dub VQRNN, to find approximate optima in the time series forecasting. Motivated by the variational quantum algorithms, we train classical activation functions to assist quantum computing. Here, unlike the quantum tensor networks (QTN) algorithm that predicts a single output feature with a single time step, our algorithm can forecast multi-output features by adjusting the recurrent hidden state. Finally, we deploy the QTN and VQRNN algorithms on the Origin Quantum platform with the numerical simulator backends using the Meteorological data set. Experimental results show that the atmospheric pressure prediction accuracy of VQRNN is 99.8864% in multi-feature forecasting tasks. In addition, we conclude that the variation-based model has an excellent performance in multi-feature output forecasting.