Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms

TL;DR

This paper investigates how different quantum phases, thermalized and many-body localized, affect the trainability and expressivity of analog variational quantum algorithms, proposing an MBL-based initialization to improve scalability.

Contribution

It introduces a novel approach connecting quantum phases of matter to VQA trainability and proposes an MBL initialization strategy to mitigate barren plateaus.

Findings

Both phases achieve maximal expressivity at large M.

Barren plateaus appear at smaller M in the thermalized phase.

MBL initialization improves initial trainability while maintaining expressivity.

Abstract

Variational quantum algorithms (VQAs) promise near-term quantum advantage, yet parametrized quantum states commonly built from the digital gate-based approach often suffer from scalability issues such as barren plateaus, where the loss landscape becomes flat. We study an analog VQA ans\"atze composed of $M$ quenches of a disordered Ising chain, whose dynamics is native to several quantum simulation platforms. By tuning the disorder strength we place each quench in either a thermalized phase or a many-body-localized (MBL) phase and analyse (i) the ans\"atze's expressivity and (ii) the scaling of loss variance. Numerics shows that both phases reach maximal expressivity at large $M$, but barren plateaus emerge at far smaller $M$ in the thermalized phase than in the MBL phase. Exploiting this gap, we propose an MBL initialisation strategy: initialise the ans\"atze in the MBL regime at intermediate quench $M$, enabling an initial trainability while retaining sufficient expressivity for subsequent optimization. The results link quantum phases of matter and VQA trainability, and provide practical guidelines for scaling analog-hardware VQAs.