A Quantum Reservoir Architecture for Chaotic Forecasting and a Test of Whether Its High Dimension Helps
Quantum reservoir computing uses a fixed quantum circuit as a feature generator and trains only a simple linear readout on top of it. This makes it cheap to train and free of the optimisation problems that affect many quantum machine-learning models. A natural worry is that the very large feature space the circuit produces might inflate apparent performance without adding anything real. This paper provides two things. First, it gives a complete, reproducible recipe for one such reservoir applied to forecasting chaotic systems, including how data is fed in, how the circuit is built, and how the readout is trained. Second, it gives a way to tell whether the reservoir's high dimension is act...
| Authors | Tushar Pandey |
|---|---|
| Primary category | quant-ph |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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