🤖 AI Summary
This study addresses the nonlinear time-series prediction task of the NARMA-10 benchmark. We systematically compare quantum reservoir computing (QRC), classical echo state networks (ESNs), long short-term memory (LSTM) networks, and a hybrid quantum-classical QLSTM under a unified experimental framework. Prediction accuracy is evaluated via normalized root-mean-square error (NRMSE), while computational resource consumption and inference latency are rigorously quantified—establishing, for the first time, a sustainability-oriented evaluation paradigm for quantum time-series modeling. Results demonstrate that QRC achieves prediction accuracy comparable to LSTM and ESN, yet with substantially reduced parameter count, memory footprint, and forward-inference latency—yielding superior energy efficiency. This work not only validates QRC’s practical viability in resource-constrained edge environments but also pioneers a green, low-overhead pathway for quantum-enhanced time-series modeling.
📝 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.