Putting machine learning to the test in a quantum many-body system

TL;DR

This paper demonstrates that advanced machine learning models can accurately predict complex properties of quantum many-body systems, including ground states and phase behaviors, surpassing previous energy estimation approaches.

Contribution

The study introduces physics-informed neural network techniques and extensive testing on the Bose-Hubbard model, significantly improving accuracy and qualitative predictions of many-body quantum states.

Findings

Achieved ground-state energy errors reduced by orders of magnitude.

Wave-function fidelities exceeded 99%.

Reproduced localization, delocalization, and multifractality trends across interaction regimes.

Abstract

Quantum many-body systems pose a formidable computational challenge due to the exponential growth of their Hilbert space. While machine learning (ML) has shown promise as an alternative paradigm, most applications remain at the proof-of-concept stage, focusing narrowly on energy estimation at the lower end of the spectrum. Here, we push ML beyond this frontier by extensively testing HubbardNet, a deep neural network architecture for the Bose-Hubbard model. Pushing improvements in the optimizer and learning rates, and introducing physics-informed output activations that can resolve extremely small wave-function amplitudes, we achieve ground-state energy errors reduced by orders of magnitude and wave-function fidelities exceeding 99%. We further assess physical relevance by analysing generalized inverse participation ratios and multifractal dimensions for ground and excited states in one and two dimensions, demonstrating that optimized ML models reproduce localization, delocalization, and multifractality trends across the spectrum. Crucially, these qualitative predictions remain robust across four decades of the interaction strength, e.g. spanning across superfluid, Mott-insulating, as well as quantum chaotic regimes. Together, these results suggest ML as a viable qualitative predictor of many-body structure, complementing the quantitative strengths of exact diagonalization and tensor-network methods.