Foundation Neural-Networks Quantum States as a Unified Ansatz for Multiple Hamiltonians

TL;DR

This paper introduces Foundation Neural-Network Quantum States (FNQS), a versatile neural network framework that generalizes across multiple quantum Hamiltonians, enabling efficient computation and analysis of complex quantum many-body systems.

Contribution

The paper presents FNQS, a unified neural network architecture that generalizes across different quantum Hamiltonians and tasks, unlike specialized models for individual systems.

Findings

FNQS can generalize to unseen Hamiltonians.

Efficient estimation of disorder-averaged observables.

Easy extraction of quantum phase transition indicators.

Abstract

Foundation models are highly versatile neural-network architectures capable of processing different data types, such as text and images, and generalizing across various tasks like classification and generation. Inspired by this success, we propose Foundation Neural-Network Quantum States (FNQS) as an integrated paradigm for studying quantum many-body systems. FNQS leverage key principles of foundation models to define variational wave functions based on a single, versatile architecture that processes multimodal inputs, including spin configurations and Hamiltonian physical couplings. Unlike specialized architectures tailored for individual Hamiltonians, FNQS can generalize to physical Hamiltonians beyond those encountered during training, offering a unified framework adaptable to various quantum systems and tasks. FNQS enable the efficient estimation of quantities that are traditionally challenging or computationally intensive to calculate using conventional methods, particularly disorder-averaged observables. Furthermore, the fidelity susceptibility can be easily obtained to uncover quantum phase transitions without prior knowledge of order parameters. These pretrained models can be efficiently fine-tuned for specific quantum systems. The architectures trained in this paper are publicly available at https://huggingface.co/nqs-models, along with examples for implementing these neural networks in NetKet.