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Quantum Optimization Quantum Machine Learning

Sustainable NARMA-10 Benchmarking for Quantum Reservoir Computing

arXiv

Authors: Avyay Kodali, Priyanshi Singh, Pranay Pandey, Krishna Bhatia, Shalini Devendrababu, Srinjoy Ganguly

Year

2025

Paper ID

50705

Status

Preprint

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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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This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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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...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2510.25183 [2] arXiv https://arxiv.org/abs/2510.25183 [3] Publisher https://arxiv.org/abs/2510.25183

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