Scalable Quantum Reservoir Computing over Distributed Quantum Architectures
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911652573609984 |
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| author | Liliopoulos, Ioannis Varsamis, Georgios D. Rallis, Konstantinos Tsipas, Evangelos Karafyllidis, Ioannis G. Sirakoulis, Georgios Ch. Dimitrakis, Panagiotis |
| author_facet | Liliopoulos, Ioannis Varsamis, Georgios D. Rallis, Konstantinos Tsipas, Evangelos Karafyllidis, Ioannis G. Sirakoulis, Georgios Ch. Dimitrakis, Panagiotis |
| contents | Reservoir computing provides an alternative to recurrent neural networks by overcoming the common problems of backpropagation through time and by training only a simple readout layer. The emerging field of quantum computing offers a new computing paradigm that promises to enhance learning through richer feature representations. In this work, we investigate quantum reservoir computing for time-series forecasting. We explore and benchmark four different architectures that combine single or multiple (distributed) reservoirs with single or multiple (distributed) ridge-regression readout layers. We evaluate these architectures using ideal and hardware-informed noisy simulations, and include both hybrid and fully quantum variants, with classical reservoir counterparts serving as a baseline. The results indicate that quantum-enhanced configurations consistently improve forecasting accuracy by reducing the mean absolute error (MAE) and the root mean squared error (RMSE) up to 78.8% and 72.3%, respectively, while distributed architectures effectively enable scaling by utilizing multiple quantum resources in a hardware-agnostic manner. These findings support distributed quantum reservoir computing as a promising, modular approach for forecasting on the quantum platforms of the noisy intermediate-scale quantum (NISQ) era. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04991 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scalable Quantum Reservoir Computing over Distributed Quantum Architectures Liliopoulos, Ioannis Varsamis, Georgios D. Rallis, Konstantinos Tsipas, Evangelos Karafyllidis, Ioannis G. Sirakoulis, Georgios Ch. Dimitrakis, Panagiotis Quantum Physics Reservoir computing provides an alternative to recurrent neural networks by overcoming the common problems of backpropagation through time and by training only a simple readout layer. The emerging field of quantum computing offers a new computing paradigm that promises to enhance learning through richer feature representations. In this work, we investigate quantum reservoir computing for time-series forecasting. We explore and benchmark four different architectures that combine single or multiple (distributed) reservoirs with single or multiple (distributed) ridge-regression readout layers. We evaluate these architectures using ideal and hardware-informed noisy simulations, and include both hybrid and fully quantum variants, with classical reservoir counterparts serving as a baseline. The results indicate that quantum-enhanced configurations consistently improve forecasting accuracy by reducing the mean absolute error (MAE) and the root mean squared error (RMSE) up to 78.8% and 72.3%, respectively, while distributed architectures effectively enable scaling by utilizing multiple quantum resources in a hardware-agnostic manner. These findings support distributed quantum reservoir computing as a promising, modular approach for forecasting on the quantum platforms of the noisy intermediate-scale quantum (NISQ) era. |
| title | Scalable Quantum Reservoir Computing over Distributed Quantum Architectures |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2605.04991 |