Stoquasticity is not enough: towards a sharper diagnostic for Quantum Monte Carlo simulability

TL;DR

This paper introduces Vanishing Geometric Phases as a new, more effective diagnostic for the quantum Monte Carlo sign problem, surpassing traditional stoquasticity-based methods in identifying sign-problem-free Hamiltonians.

Contribution

It develops the VGP criterion for diagnosing QMC simulability, analyzes its computational complexity, and proposes VGP-inspired quantitative diagnostics for sign problem severity.

Findings

VGP criterion can identify sign-problem-free Hamiltonians more effectively than stoquasticity.

Certain Hamiltonians are easily recognized as sign-problem-free via VGP despite being non-stoquastic.

VGP-inspired diagnostics can analyze the scaling of the sign problem under transformations.

Abstract

Quantum Monte Carlo (QMC) methods are powerful tools for simulating quantum many-body systems, yet their applicability is limited by the infamous sign problem. We approach this challenge through the lens of Vanishing Geometric Phases (VGP) \cite{Hen_2021}, introducing it as a `geometric' criterion for diagnosing QMC simulability. We characterize the class of VGP Hamiltonians, and analyze the complexity of recognizing this class, identifying both hard and efficiently identifiable cases. We further highlight the practical advantage of the VGP criterion by exhibiting specific Hamiltonians that are readily identified as sign-problem-free through VGP, yet whose stoquasticity is difficult to ascertain. These examples underscore the efficiency and sharpness of VGP as a diagnostic tool compared to stoquasticity-based heuristics. Beyond classification, we propose a family of VGP-inspired diagnostics that serve as quantitative indicators of sign problem severity. While exact evaluation of these quantities is generically intractable, we demonstrate their mathematical power in performing scaling analysis for the average sign under unitary transformations. Our results provide both a conceptual foundation and practical tools for understanding and mitigating the sign problem.