The authors propose gated QKAN-FWP, a recurrent architecture that uses single-qubit quantum circuits (or classical simulations of them) as learnable nonlinear activations in a Fast Weight Programmer framework. Instead of multi-qubit entanglement, they use "data re-uploading" on single qubits, which is easier to simulate classically and run on noisy quantum hardware. They test it on time-series tasks, MiniGrid RL, and solar-cycle forecasting (528-month input, 132-month forecast), where their 12.5k-parameter model beats LSTM baselines with up to 13× more parameters and runs successfully on IonQ and IBM quantum processors.
Main takeaways:
- Uses single-qubit quantum circuits as activation functions in a fast-weight recurrent framework, avoiding expensive multi-qubit gates
- Introduces a scalar-gated update rule that stabilizes parameter evolution and enables parallelizable gradients
- On solar-cycle forecasting, a 12.5k-parameter model outperforms LSTM baselines with 25.9k–167k parameters on MSE and peak errors
- Deployed on real quantum hardware (IonQ, IBM) and recovered accuracy within 0.1% relative MSE at 1024 shots