Intrinsic dimension of path integrals: data mining quantum criticality and emergent simplicity

TL;DR

This paper introduces a method to analyze quantum many-body systems by examining the intrinsic dimension and local distance variance of path integral data, revealing universal patterns near quantum critical points and the impact of symmetries.

Contribution

It presents a novel data mining approach using intrinsic dimension and nearest neighbor variance to characterize quantum criticality in path integral manifolds, highlighting universal scaling and symmetry effects.

Findings

Path integral manifolds exhibit universal scaling near quantum critical points.

Intrinsic dimension and NN-distance variance reveal data structure simplification at phase transitions.

Non-Abelian symmetries significantly affect quantum data structures.

Abstract

Quantum many-body systems are characterized by patterns of correlations that define highly-non trivial manifolds when interpreted as data structures. Physical properties of phases and phase transitions are typically retrieved via simple correlation functions, that are related to observable response functions. Recent experiments have demonstrated capabilities to fully characterize quantum many-body systems via wave-function snapshots, opening new possibilities to analyze quantum phenomena. Here, we introduce a method to data mine the correlation structure of quantum partition functions via their path integral (or equivalently, stochastic series expansion) manifold. We characterize path-integral manifolds generated via state-of-the-art Quantum Monte Carlo methods utilizing the intrinsic dimension (ID) and the variance of distances from nearest neighbors (NN): the former is related to dataset complexity, while the latter is able to diagnose connectivity features of points in configuration space. We show how these properties feature universal patterns in the vicinity of quantum criticality, that reveal how data structures {\it simplify} systematically at quantum phase transitions. This is further reflected by the fact that both ID and variance of NN-distances exhibit universal scaling behavior in the vicinity of second-order and Berezinskii-Kosterlitz-Thouless critical points. Finally, we show how non-Abelian symmetries dramatically influence quantum data sets, due to the nature of (non-commuting) conserved charges in the quantum case. Complementary to neural network representations, our approach represents a first, elementary step towards a systematic characterization of path integral manifolds before any dimensional reduction is taken, that is informative about universal behavior and complexity, and can find immediate application to both experiments and Monte Carlo simulations.