QuIRK: Quantum-Inspired Re-uploading KAN
This is an incremental improvement for researchers in machine learning and quantum-inspired computing, enhancing model efficiency and interpretability in scientific domains.
The paper tackled the problem of improving Kolmogorov-Arnold Networks (KANs) by introducing a quantum-inspired variant called QuIRK, which replaces B-Splines with single-qubit quantum data re-uploading models, resulting in matching or outperforming traditional KANs with even fewer parameters, especially for periodic functions.
Kolmogorov-Arnold Networks or KANs have shown the ability to outperform classical Deep Neural Networks, while using far fewer trainable parameters for regression problems on scientific domains. Even more powerful has been their interpretability due to their structure being composed of univariate B-Spline functions. This enables us to derive closed-form equations from trained KANs for a wide range of problems. This paper introduces a quantum-inspired variant of the KAN based on Quantum Data Re-uploading (DR) models. The Quantum-Inspired Re-uploading KAN or QuIRK model replaces B-Splines with single-qubit DR models as the univariate function approximator, allowing them to match or outperform traditional KANs while using even fewer parameters. This is especially apparent in the case of periodic functions. Additionally, since the model utilizes only single-qubit circuits, it remains classically tractable to simulate with straightforward GPU acceleration. Finally, we also demonstrate that QuIRK retains the interpretability advantages and the ability to produce closed-form solutions.