Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning

Authors: Kuo-Chung Peng, Samuel Yen-Chi Chen, Jiun-Cheng Jiang et al.

arXiv · PDF

Summary

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

Relevance

Not related to my persona or midtraining work—this is a specialized recurrent architecture for time-series and RL tasks, with a focus on quantum-circuit activations. Interesting for general ML curiosity but doesn't touch language models, prompt–steering equivalence, or behavior installation.

Abstract

arXiv:2605.06734v1 Announce Type: new Abstract: Fast Weight Programmers (FWPs) encode temporal dependencies through dynamically updated parameters rather than recurrent hidden states. Quantum FWPs (QFWPs) extend this idea with variational quantum circuits (VQCs), but existing implementations rely on multi-qubit architectures that are difficult to scale on noisy intermediate-scale quantum (NISQ) devices and expensive to simulate classically. We propose gated QKAN-FWP, a fast-weight framework that integrates FWP with Quantum-inspired Kolmogorov-Arnold Network (QKAN) using single-qubit data re-uploading circuits as learnable nonlinear activation, known as DatA Re-Uploading ActivatioN (DARUAN). We further introduce a scalar-gated fast-weight update rule that stabilizes parameter evolution, supported by a theoretical analysis of its adaptive memory kernel, geometric boundedness, and parallelizable gradient paths. We evaluate the framework across time-series benchmarks, MiniGrid reinforcement learning, and highlight real-world solar cycle forecasting as our main practical result. In the long-horizon setting with 528-month input window and 132-month forecast horizon, our 12.5k-parameter model achieves lower scaled Mean Square Error (MSE), peak amplitude error, and peak timing error than a suite of classical recurrent baselines with up to 13x more parameters, including Long Short-Term Memory (LSTM) networks (25.9k-89.1k parameters), WaveNet-LSTM (167k), Vanilla recurrent neural network (11.5k), and a Modified Echo State Network (132k). To validate NISQ compatibility, we further deploy the trained fast programmer on IonQ and IBM Quantum processors, recovering forecasting accuracy within 0.1% relative MSE of the noiseless simulator at 1024 shots. These results position gated QKAN-FWP as a scalable, parameter-efficient, and NISQ-compatible approach to quantum-inspired sequence modeling.