Warm Starts, Cold States: Exploiting Adiabaticity for Variational Ground-States

Understand

Reliable preparation of many-body ground states is an essential task in quantum computing, with applications spanning areas from chemistry and materials modeling to quantum optimization and benchmarking.

• A variety of approaches have been proposed to tackle this problem, including variational methods.
• However, variational training often struggle to navigate complex energy landscapes, frequently encountering suboptimal local minima or suffering from barren plateaus.
• In this work, we introduce an iterative strategy for ground-state preparation based on a stepwise (discretized) Hamiltonian deformation.