Neural Polaron: Learning Quasiparticle Operators in Quantum Many-Body Systems

Shang-Shun Zhang

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arXiv · 2606.22644

Neural Polaron: Learning Quasiparticle Operators in Quantum Many-Body Systems

Shang-Shun Zhang

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Abstract

Understanding dynamical properties of quantum many-body systems remains a central challenge because excitations generally require information beyond a ground-state wave function. Here we introduce a neural polaron ansatz that represents quasiparticle excitations by neural many-body operators acting on a correlated ground state. Instead of learning an independent excited-state wave function, the method parameterizes a local dressing operator through a compact neural head defined on the feature map of a pretrained ground-state network. This operator-based construction builds in translation symmetry, momentum resolution, and quasiparticle locality, while separating ground-state correlations from excitation-specific dressing. We benchmark the method on the square-lattice $J_1$-$J_2$ Heisenberg model, where it accurately reproduces magnon dispersions and spectral weights over a broad range of frustration. In particular, it captures nontrivial many-body features including the $(\pi,0)$ anomaly and its progressive softening with increasing $J_2/J_1$. These results establish neural operators as a physically transparent route for extending neural quantum states from ground-state properties to dynamical response.

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Shang-Shun Zhang. 2026-06-21. Neural Polaron: Learning Quasiparticle Operators in Quantum Many-Body Systems. https://arxiv.org/abs/2606.22644

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