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arXiv · 2603.03159

Solving sign problems with physics-informed kernels

Friederike Ihssen·Renzo Kapust·Jan M. Pawlowski
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Abstract

In the present work we construct a novel generative architecture for systems with complex probability distributions. In general, these sampling tasks come with two challenges: resolving sign problems and efficient sampling. The architecture is based on physics-informed kernels (PIKs) introduced in arXiv:2510.26678, and aims at resolving both challenges. Key to the complex PIK-architecture is its probability-weight preserving property, which allows us to map the sampling task to one on a sign-problem free manifold with a simple distribution and efficient sampling. The potential of this novel architecture is demonstrated within applications to zero-dimensional field theories with complex couplings, as well as the real-time evolution of the quantum-mechanical harmonic oscillator.

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Friederike Ihssen, Renzo Kapust, Jan M. Pawlowski. 2026-03-03. Solving sign problems with physics-informed kernels. https://arxiv.org/abs/2603.03159

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