Connecting phases of matter to the flatness of the loss landscape in analog variational quantum algorithms
This work addresses scalability issues in near-term quantum computing for researchers in quantum algorithms, offering incremental improvements by linking quantum phases to trainability.
The study tackled the problem of barren plateaus in variational quantum algorithms by analyzing analog ansätze based on quenches in disordered Ising chains, finding that many-body-localized phases delay barren plateaus compared to thermalized phases, enabling a trainable initialization strategy.
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 ansätze 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 ansätze'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 ansätze 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.