Quantum Reservoir Computing: Recent Advances and Future Directions
Quantum reservoir computing (QRC) uses the dynamics of a fixed or weakly tuned quantum system to transform temporal and sequential inputs into measured features, while training is typically confined to a classical readout. This separation reduces reliance on repeated quantum parameter updates and avoids the barren plateaus associated with variational circuit training. Its computational power is often attributed to the exponentially large Hilbert space of the quantum system. However, the memory, nonlinearity, and expressivity that determine what a reservoir can actually compute depend jointly on the input encoding, quantum evolution, observables, measurement, and readout, not on Hilbert sp...
| Authors | Shehbaz Tariq, Muhammad Talha, Arshid Ali et al. |
|---|---|
| Primary category | quant-ph |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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