Predicting chaotic dynamics on NISQ hardware with quantum reservoir networks

Abstract Recent advances in artificial intelligence have highlighted the remarkable capabilities of neural network (NN)-powered systems on classical computers. However, these systems face significant computational challenges that limit scalability and efficiency. Here, we propose a quantum reservoir network (QRN) algorithm for prediction and reconstruction of dynamical systems with current quantum hardware. This is developed from the recent NISQRC framework to imbue quantum circuits with a practical fading memory, and we demonstrate its effectiveness on an IBM quantum processor. We apply classical control-theoretic response analysis to characterize the QRN, emphasizing its rich nonlinear dynamics and memory, as well as its ability to be fine-tuned with sparsity and re-uploading blocks. Noisy and noiseless simulations, as well as IBM hardware experiments, demonstrate the capability of our QRN to reconstruct unknown latent variables of the Lorenz system at future timesteps. Our results show that the QRN can operate with persistent memory for over 100 times longer than the median $${{{{\mathcal{T}}}}}_{1}$$ T 1 and $${{{{\mathcal{T}}}}}_{2}$$ T 2 of the QPU, achieving state-of-the-art time-series performance on IBM hardware.