Speaker
Description
Quantum reservoir computing processes temporal data with fixed quantum dynamics and a trained classical readout. We convert the hardware-tested kicked-Ising circuit family used in dual-unitary chaos simulations and large-scale quantum extreme learning machines into an online quantum reservoir. Unlike feedforward QELM feature maps, the circuit is recurrent. A visible register is measured and reset at each time step, while hidden qubits carry memory across the input stream.
Exact noiseless simulations reveal a structured operating regime, not a single optimum. Memory capacity forms a broad intermediate band, observable variability peaks elsewhere, and many-body scrambling increases toward the dual-unitary corner. We choose a compromise regime where memory is high, Pauli features remain resolvable, and the dynamics remain nontrivial. Observable variability acts as an anti-concentration diagnostic, and tensor-network bond-dimension diagnostics provide a compressibility check.
The memory analysis separates memory formation from memory readout. The hidden subsystem retains finite fading memory whose timescale increases with hidden-register size over the checked range. Observable-subset sweeps show that this extra memory becomes recoverable only when the measured Pauli feature space is sufficiently rich. Scaling therefore requires both recurrent memory and observable access.
We implement the selected reservoir on IBM superconducting hardware with repeated mid-circuit measurement and reset. Our short-term memory capacity and NARMA task results using exact, finite-shot simulated, and hardware features show that short-delay memory survives on hardware. Longer-horizon recovered memory is limited by finite-shot precision and device-level distortions beyond sampling noise.
| Topic | Quantum algorithms for machine learning applications |
|---|---|
| Additional Topic | Hybrid quantum-classical approaches for learning and optimization |