Multivariate Time Series Forecasting with Gate-Based Quantum Reservoir Computing on NISQ Hardware
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915556119019520 |
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| author | Hamhoum, Wissal Cherkaoui, Soumaya Laprade, Jean-Frederic Ahmed, Ola Wang, Shengrui |
| author_facet | Hamhoum, Wissal Cherkaoui, Soumaya Laprade, Jean-Frederic Ahmed, Ola Wang, Shengrui |
| contents | Quantum reservoir computing (QRC) offers a hardware-friendly approach to temporal learning, yet most studies target univariate signals and overlook near-term hardware constraints. This work introduces a gate-based QRC for multivariate time series (MTS-QRC) that pairs injection and memory qubits and uses a Trotterized nearest-neighbor transverse-field Ising evolution optimized for current device connectivity and depth. On Lorenz-63 and ENSO, the method achieves a mean square error (MSE) of 0.0087 and 0.0036, respectively, performing on par with classical reservoir computing on Lorenz and above learned RNNs on both, while NVAR and clustered ESN remain stronger on some settings. On IBM Heron R2, MTS-QRC sustains accuracy with realistic depths and, interestingly, outperforms a noiseless simulator on ENSO; singular value analysis indicates that device noise can concentrate variance in feature directions, acting as an implicit regularizer for linear readout in this regime. These findings support the practicality of gate-based QRC for MTS forecasting on NISQ hardware and motivate systematic studies on when and how hardware noise benefits QRC readouts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13634 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multivariate Time Series Forecasting with Gate-Based Quantum Reservoir Computing on NISQ Hardware Hamhoum, Wissal Cherkaoui, Soumaya Laprade, Jean-Frederic Ahmed, Ola Wang, Shengrui Machine Learning Emerging Technologies Quantum reservoir computing (QRC) offers a hardware-friendly approach to temporal learning, yet most studies target univariate signals and overlook near-term hardware constraints. This work introduces a gate-based QRC for multivariate time series (MTS-QRC) that pairs injection and memory qubits and uses a Trotterized nearest-neighbor transverse-field Ising evolution optimized for current device connectivity and depth. On Lorenz-63 and ENSO, the method achieves a mean square error (MSE) of 0.0087 and 0.0036, respectively, performing on par with classical reservoir computing on Lorenz and above learned RNNs on both, while NVAR and clustered ESN remain stronger on some settings. On IBM Heron R2, MTS-QRC sustains accuracy with realistic depths and, interestingly, outperforms a noiseless simulator on ENSO; singular value analysis indicates that device noise can concentrate variance in feature directions, acting as an implicit regularizer for linear readout in this regime. These findings support the practicality of gate-based QRC for MTS forecasting on NISQ hardware and motivate systematic studies on when and how hardware noise benefits QRC readouts. |
| title | Multivariate Time Series Forecasting with Gate-Based Quantum Reservoir Computing on NISQ Hardware |
| topic | Machine Learning Emerging Technologies |
| url | https://arxiv.org/abs/2510.13634 |