Sustainable NARMA-10 Benchmarking for Quantum Reservoir Computing

Avyay Kodali, Priyanshi Singh, Pranay Pandey, Krishna Bhatia, Shalini Devendrababu +1 more
10/27/2025
quant-phcs.LG

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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.

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Cite this paper

@article{kodali2025sustainable,
  title  = {Sustainable NARMA-10 Benchmarking for Quantum Reservoir Computing},
  author = {Avyay Kodali and Priyanshi Singh and Pranay Pandey and Krishna Bhatia and Shalini Devendrababu and Srinjoy Ganguly},
  year   = {2025},
  eprint = {2510.25183},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.25183v1}
}

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