Connectivity determines the capability of sparse neural network quantum states

TL;DR

This paper extends the Lottery Ticket Hypothesis to quantum many-body problems, showing that sparse neural networks can effectively approximate ground states of quantum Hamiltonians, with performance depending on network structure rather than initialization.

Contribution

It demonstrates that sparse neural networks can accurately model quantum ground states, revealing a structure-dependent performance and uncovering universal scaling and phase transition phenomena.

Findings

Sparse networks match dense model accuracy in quantum tasks

Performance depends on network structure, not initialization

Identifies universal scaling and phase transition signatures

Abstract

The Lottery Ticket Hypothesis (LTH) posits that within overparametrized neural networks, there exist sparse subnetworks that are capable of matching the performance of the original model when trained in isolation from the original initialization. We extend this hypothesis to the unsupervised task of approximating the ground state of quantum many-body Hamiltonians, a problem equivalent to finding a neural-network compression of the lowest-lying eigenvector of an exponentially large matrix. Focusing on two representative quantum Hamiltonians, the transverse field Ising model (TFIM) and the toric code (TC), we demonstrate that sparse neural networks can reach accuracies comparable to their dense counterparts, even when pruned by more than an order of magnitude in parameter count. Crucially, and unlike the original LTH, we find that performance depends only on the structure of the sparse subnetwork, not on the specific initialization, when trained in isolation. Moreover, we identify universal scaling behavior that persists across network sizes and physical models, where the boundaries of scaling regions are determined by the underlying Hamiltonian. At the onset of high-error scaling, we observe signatures of a sparsity-induced quantum phase transition that is first-order in shallow networks. Finally, we demonstrate that pruning enhances interpretability by linking the structure of sparse subnetworks to the underlying physics of the Hamiltonian.