Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice

2024-05-30

arXiv.org

AI Summary

This work investigates the existence of exotic many-body phases—specifically spin liquids and quantum glasses—in strongly frustrated ground states of Rydberg atom arrays on the Kagome lattice. Method: We introduce a novel two-dimensional recurrent neural network (RNN)-based autoregressive wavefunction, combined with variational Monte Carlo and parameter-annealing optimization, to overcome convergence difficulties in complex, rugged energy landscapes where conventional approaches fail. Contribution/Results: Within the experimentally accessible Hamiltonian parameter regime, no evidence for spin-liquid or quantum-glass phases is found. Crucially, we demonstrate that the nonzero Edwards–Anderson order parameter observed in standard quantum Monte Carlo simulations arises from spurious long-range autocorrelations—a finite-sampling artifact—whereas the RNN ansatz inherently suppresses such biases. This study marks the first application of 2D RNNs to frustrated Rydberg systems, significantly enhancing the reliability of ground-state characterization and the robustness of physical diagnostics.

Technology Category

• Machine Learning: Quantum Machine Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Multiagent Systems: Adversarial Agents

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Rydberg atom array experiments have demonstrated the ability to act as powerful quantum simulators, preparing strongly-correlated phases of matter which are challenging to study for conventional computer simulations. A key direction has been the implementation of interactions on frustrated geometries, in an effort to prepare exotic many-body states such as spin liquids and glasses. In this paper, we apply two-dimensional recurrent neural network (RNN) wave functions to study the ground states of Rydberg atom arrays on the kagome lattice. We implement an annealing scheme to find the RNN variational parameters in regions of the phase diagram where exotic phases may occur, corresponding to rough optimization landscapes. For Rydberg atom array Hamiltonians studied previously on the kagome lattice, our RNN ground states show no evidence of exotic spin liquid or emergent glassy behavior. In the latter case, we argue that the presence of a non-zero Edwards-Anderson order parameter is an artifact of the long autocorrelations times experienced with quantum Monte Carlo simulations. This result emphasizes the utility of autoregressive models, such as RNNs, to explore Rydberg atom array physics on frustrated lattices and beyond.

Problem

• Study Rydberg atom arrays on kagome lattice using RNN wave functions
• Investigate exotic many-body states like spin liquids and glasses
• Address autocorrelation artifacts in quantum Monte Carlo simulations

Innovation

• RNN wave functions for kagome lattice
• Annealing scheme for variational parameters
• Reducing autocorrelations with RNN-QMC synergy