Local Relaxation Hierarchies for Quantum Ground State Energies: Convergence Guarantees and Message Passing Algorithms

2026-09-30

AI Summary

This study addresses the high computational cost and lack of efficiency guarantees in convex relaxation methods for computing ground-state energies of large-scale quantum many-body systems. To this end, it proposes a local relaxation hierarchy coupled with an efficient parallel message-passing algorithm. Methodologically, this work establishes the first proof of exactness for tree-structured exchange Hamiltonians and proves exponential convergence for weakly perturbed chain systems. Furthermore, it constructs a solver framework integrating entropy-smoothed objectives with Nesterov-accelerated gradient descent. The resulting algorithm achieves a complexity of O(n/ε) and significantly outperforms standard solvers across various quantum systems, enabling efficient estimation of ground-state energy lower bounds in polynomial time. The associated code has been made publicly available.

Abstract

Convex relaxation hierarchies provide lower bounds to the ground state energy of quantum many-body systems that can be computed in polynomial time on a classical computer, at any fixed hierarchy level. However, scaling these methods to large systems and accurate approximations remains challenging due to the computational cost of traditional solvers and the scarcity of efficiency guarantees. In this work, we develop local relaxation hierarchies and efficient, highly parallelisable message passing algorithms for estimating the relaxed ground state energies. We show that the first level of the hierarchy---based on local consistency of pairwise reduced density matrices---is exact for commuting Hamiltonians on trees. We further establish that another hierarchy, based on consistent intervals, converges exponentially fast in the interval size to the ground state energy for weak perturbations of separable Hamiltonians on a chain, thereby providing an efficient classical algorithm for these systems. Then, we introduce two variants of message passing algorithms that run in $\mathcal{O}(n/ε^2)$ and $\mathcal{O}(n/ε)$ time for any fixed level of the local hierarchy on bounded-degree graphs, where $ε$ is the precision for the relaxed ground state energy per site. This assumes that the optimal messages have $\mathcal{O}(1)$ norm---a condition we observe in practical settings in our experiments. These algorithms are based on the subgradient method and the Nesterov-type accelerated gradient descent method applied to an entropy-smoothed objective. Finally, we benchmark the message passing algorithms across different quantum Hamiltonians, lattice geometries, and relaxation levels, validating the theoretical predictions and their potential to surpass standard convex optimisation solvers for this problem. We release the resulting library at github.com/rick1924/gse-message-passing.

Problem

• quantum ground state energy
• convex relaxation hierarchies
• quantum many-body systems
• computational scalability

Innovation

• Local Relaxation Hierarchies
• Message Passing Algorithms
• Quantum Ground State Energy
• Convex Relaxation
• Accelerated Gradient Descent