Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.25183 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914121763520512 |
|---|---|
| author | Kodali, Avyay Singh, Priyanshi Pandey, Pranay Bhatia, Krishna Devendrababu, Shalini Ganguly, Srinjoy |
| author_facet | Kodali, Avyay Singh, Priyanshi Pandey, Pranay Bhatia, Krishna Devendrababu, Shalini Ganguly, Srinjoy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25183 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sustainable NARMA-10 Benchmarking for Quantum Reservoir Computing Kodali, Avyay Singh, Priyanshi Pandey, Pranay Bhatia, Krishna Devendrababu, Shalini Ganguly, Srinjoy Quantum Physics Machine Learning 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. |
| title | Sustainable NARMA-10 Benchmarking for Quantum Reservoir Computing |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2510.25183 |