Trapped Fermions Through Kolmogorov-Arnold Wavefunctions

• URL: http://arxiv.org/abs/2512.07800v1
• Date: Mon, 08 Dec 2025 18:30:30 GMT
• Title: Trapped Fermions Through Kolmogorov-Arnold Wavefunctions
• Authors: Paulo F. Bedaque, Jacob Cigliano, Hersh Kumar, Srijit Paul, Suryansh Rajawat,
• Abstract summary: We investigate a variational Monte Carlo framework for trapped one-dimensional mixture of spin-$frac12$ fermions.<n>We construct universal neural-network wavefunction anstze using Kolmogorov-Arnold networks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 ansätze. 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 ansätz without biasing the method.

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