Density matrix emulation of quantum recurrent neural networks for multivariate time series prediction
Quantum recurrent neural networks (QRNNs) are robust candidates for modelling and predicting future values in multivariate time series.However, the effective implementation of some QRNN models is limited by the need for mid-circuit measurements.Those increase the requirements for quantum hardware, which in the current noisy intermediate-scale quant