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 \({{{{\mathcal{T}}}}}_{1}\) and \({{{{\mathcal{T}}}}}_{2}\) of the ibm_marrakesh QPU, achieving state-of-the-art time-series performance on IBM hardware.
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Acknowledgements
We E.L.C. and V.N. would like to acknowledge the funding support from the Naval Sea Systems Command, Naval Surface Warfare Center, Panama City Division (NSWC- PCD) under the Naval Engineering Education Consortium (NEEC) Grant Program (award #N00174-23-1-0006). E.N.E. would like to acknowledge funding from the Naval Innovation Science and Engineering (NISE) program. The entire team would like to acknowledge the support of the IBM Quantum team for providing valuable feedback and suggestions in the process of making these experiments run on hardware. Any opinions, findings, conclusions, or recommendations expressed here are those of the authors and do not necessarily reflect the views of the aforementioned groups.
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Connerty, E.L., Evans, E.N., Angelatos, G. et al. Predicting chaotic dynamics on NISQ hardware with quantum reservoir networks. Commun Phys (2026). https://doi.org/10.1038/s42005-026-02652-1
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DOI: https://doi.org/10.1038/s42005-026-02652-1
