{"ID":2849634,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.25183","arxiv_id":"2510.25183","title":"Sustainable NARMA-10 Benchmarking for Quantum Reservoir Computing","abstract":"This study compares Quantum Reservoir Computing (QRC) with classical models such as Echo State Networks (ESNs) and Long Short-Term Memory networks (LSTMs), as well as hybrid quantum-classical architectures (QLSTM), for the nonlinear autoregressive moving average task (NARMA-10). We evaluate forecasting accuracy (NRMSE), computational cost, and evaluation time. Results show that QRC achieves competitive accuracy while offering potential sustainability advantages, particularly in resource-constrained settings, highlighting its promise for sustainable time-series AI applications.","short_abstract":"This study compares Quantum Reservoir Computing (QRC) with classical models such as Echo State Networks (ESNs) and Long Short-Term Memory networks (LSTMs), as well as hybrid quantum-classical architectures (QLSTM), for the nonlinear autoregressive moving average task (NARMA-10). We evaluate forecasting accuracy (NRMSE)...","url_abs":"https://arxiv.org/abs/2510.25183","url_pdf":"https://arxiv.org/pdf/2510.25183v1","authors":"[\"Avyay Kodali\",\"Priyanshi Singh\",\"Pranay Pandey\",\"Krishna Bhatia\",\"Shalini Devendrababu\",\"Srinjoy Ganguly\"]","published":"2025-10-27T13:46:06Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.LG\"]","methods":"[]","has_code":false}