Entanglement-informed distributed wavefunction approach to scalable quantum many-body systems

TL;DR

This paper introduces an entanglement-based distributed wavefunction approach that leverages the entanglement structure of quantum states to enable scalable and efficient simulation of large quantum many-body systems.

Contribution

It proposes a novel representation based on entanglement cuts that simplifies Hamiltonian operations and demonstrates near-linear scaling in large systems, unifying various simulation methods.

Findings

Near-linear scaling achieved for large quantum systems.

Entanglement spectrum fragmentation influences computational cost.

The approach unifies different methods under an entanglement-based framework.

Abstract

We show that the entanglement structure of quantum many-body states defines a natural and optimal distributed representation for their simulation. An arbitrary entanglement cut induces a bipartite decomposition of the wavefunction, mapping its distribution onto that of the entanglement spectrum. In this representation the Hamiltonian application, the core of Krylov-subspace methods, reduces to local contractions and communication-optimal operations. Using benchmarks from different methods and models, we demonstrate near-linear scaling for sufficiently large systems and identify entanglement spectrum fragmentation as a key factor controlling computational cost. This establishes entanglement as an organizing principle and unified, method-independent, route for scaling up quantum many-body simulations.