Trapped Fermions Through Kolmogorov-Arnold Wavefunctions

Paulo F. Bedaque; Jacob Cigliano; Hersh Kumar; Srijit Paul; Suryansh Rajawat

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arXiv · 2512.07800

Trapped Fermions Through Kolmogorov-Arnold Wavefunctions

Paulo F. Bedaque·Jacob Cigliano·Hersh Kumar·Srijit Paul·Suryansh Rajawat

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Abstract

We investigate a variational Monte Carlo framework for trapped one-dimensional mixture of spin-$\frac{1}{2}$ fermions using Kolmogorov-Arnold networks (KANs) to construct universal neural-network wavefunction ans\"atze. The method can, in principle, achieve arbitrary accuracy, limited only by the Monte Carlo sampling and was checked against exact results at sub-percent precision. For attractive interactions, it captures pairing effects, and in the impurity case it agrees with known results. We present a method of systematic transfer learning in the number of network parameters, allowing for efficient training for a target precision. We vastly increase the efficiency of the method by incorporating the short-distance behavior of the wavefunction into the ans\"atz without biasing the method.

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nucl-th cond-mat.dis-nn cond-mat.quant-gas physics.comp-ph quant-ph

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Paulo F. Bedaque, Jacob Cigliano, Hersh Kumar, Srijit Paul, Suryansh Rajawat. 2025-12-08. Trapped Fermions Through Kolmogorov-Arnold Wavefunctions. https://arxiv.org/abs/2512.07800

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