Recurrent Quantum Feature Maps for Reservoir Computing
Reservoir computing promises a fast method for handling large amounts of temporal data. This hinges on constructing a good reservoir--a dynamical system capable of transforming inputs into a high-dimensional representation while remembering properties of earlier data. In this work, we introduce a reservoir based on recurrent quantum feature maps where a fixed quantum circuit is reused to encode both current inputs and a classical feedback signal derived from previous outputs. We evaluate the model on the Mackey-Glass time-series prediction task using our recently introduced CP feature map, and find that it achieves lower mean squared error than standard classical baselines, including echo...
| Authors | Utkarsh Singh, Aaron Z. Goldberg, Christoph Simon et al. |
|---|---|
| Primary category | quant-ph |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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